Report of Investigative Research into Green Benefits
Enabled by the Utilization of IoT-- First Report: Logistics and Agriculture --March 2017

Green CPS/IoT Review Working Group
Green IT Committee

Japan Electronics and Information Technology Industries
Association

Table of Contents
Introduction
1.

From IT to IoT....................................................................................................................................... 1

2.

What are the Green Benefits enabled by IoT solutions? .................................................................... 4

3.

Evaluation of Green Benefits enabled by IoT solutions: -- As envisioned in this report ................. 6

4.

3.1

Evaluation methods ..................................................................................................................... 6

3.2

Items to be included in the evaluation ......................................................................................... 7

3.3

Areas evaluated (logistics and agriculture) ................................................................................. 8

Results from Trial Evaluations of Green Benefits -- Examples in Logistics and Agriculture -- ... 10
4.1

IoT utilization in logistics .......................................................................................................... 10

4.1.1 Status of IoT utilization ......................................................................................................... 10
4.1.2 Results from evaluations (trials) of specific examples .......................................................... 13[Case 1] Forklift traffic line analysis service ......................................................................... 14[Case 2] Visual representation of transport quality and analysis services using sensor logger................................................................................................................................. 16[Case 3] Remote temperature control solution for freezer/refrigerator transport vehicles .... 18[Case 4] Demand forecasts and energy-saving logistics operations through the use of big
weather data ............................................................................................................. 20
4.2

IoT utilization in agriculture ...................................................................................................... 22

4.2.1 Status of IoT utilization ......................................................................................................... 22
4.2.2 Specific examples .................................................................................................................. 23[Case 1] Contributing to the environment through the reduction of harvesting loss and crop
disposals based on harvest forecasts. (JSOL Corporation) ...................................... 23[Case 2] Improvements to work efficiency and quality through the visual representation oftemperature control know-how. (Fujitsu Limited)................................................... 24
4.3

What we have found in trial evaluations ................................................................................... 25

4.3.1 New subjects for evaluation .................................................................................................. 25
4.3.2 Factors that cause energy consumption to increase ............................................................... 26
5.

In approaching future reviews ........................................................................................................... 27

Reference Documents .................................................................................................................................. 28
Roster of members ..................................................................................................................................... 29iIntroduction
The Green IT Committee of the Japan Electronics and Information Technology Industries Association(JEITA) is engaged in activities aimed at promoting the introduction of IT products (devices, solutions, andservices) that help improve energy efficiencies in society as a way of fulfilling both environmental andeconomic needs.
Existing IT products have been put to use in specific operations, industries, and sectors, to the benefit of theenvironment in various ways. Meanwhile, we are beginning to see the utilization of novel technologies
such as IoT and AI in recent years. Because of society’s use of IoT, target areas have expanded worldwide
over the Internet and a diverse range of data is being consolidated as big data. With AI, new values arebeing created and provided beyond the frameworks of any single industry or sector.
Based on this background, the Green IT Committee established the "Green CPS/IoT Review Working
Group" in 2015 to begin assessing the various benefits that are enabled by IoT. Because IoT is expanding
into a variety of areas of society, the Working Group decided to conduct its initial evaluations of theenvironmental benefits of IoT and did reviews specifically in the areas of agriculture and logistics between
2015 and 2016. This report is a summary of the results of these trial evaluations.
Moving forward, we expect to see the integration of big data through IoT, further advancements in theutilization of AI as well as other technologies, and ongoing growth in the scope and volume of benefits
from IT products that utilize these technologies. With the aim of satisfying both environmental andeconomic needs, the Green IT Committee is committed to promoting the development and use of IT
products that can bring benefit to society. We greatly appreciate your support and input.

Osamu Namikawa
Project Manager, Green CPS/IoT Review Working Group
Green IT Committee
March 2017

ii

1.

From IT to IoT

Thanks to technological advancements in recent years, traditional IT solutions are evolving into solutions
that utilize the "Internet of things" (IoT). While computers and other IT devices were the primary devices
traditionally connected to the Internet, in IoT, electronic devices such as sensors and control devices thatare widely used in manufacturing, offices, and private residents will be connected to the Internet (Fig. 1).
As a result of IoT utilization, we can now improve efficiencies and create novel values in a much wider
scope of areas than with traditional IT solutions, as new solutions are being developed in a variety of areas
such as manufacturing, logistics, agriculture, infrastructure, and healthcare (Fig. 2). These innovations werefirst coined "Industry 4.0" mainly in European countries, and discussions primarily focused on theirintroduction in the area of manufacturing/production. However, we believe that technological innovations
such as IoT, big data, robotics, and artificial intelligence (AI) will come together as the "Fourth Industrial
Revolution 1 " to the benefit of a broad swath of industry, and will not be confined to
manufacturing/production alone. Additionally, from the demand side perspective, expectations have beenraised for an "Ultra-Smart Society" (Society 5.0) as a future society where cyber space and physical space(the real world) are highly integrated with each other, products and services are fine-tuned to address
diverse and latent needs, and both economic growth and solutions to societal issues are achieved 2.
Expectations are being raised that, as a result of this increase in the number of areas that utilize IoT, thenumber of areas that benefit from environmental improvements (CO2 emissions reductions, etc.) that are
enabled by IoT-based solutions (hereafter "IoT solutions") will continue to grow.
Traditional IT
devices

Storage

Internet

Factories

Traffic, logistics

Buildings, shops

Healthcare

Private homes

EnergyThe expansionof IoT

Fig. 1: "Things" are increasingly becoming connected to the Internet

1

Vision for New Industrial Structures -- Japan's Strategy for Leading the Fourth Industrial Revolution -- Interim Summary(Ministry of Economy, Trade and Industry)
http://www.meti.go.jp/committee/sankoushin/shin_sangyoukouzou/pdf/ch_01.pdf
2Basic Plan for Science and Technologies for the Fifth Term (Cabinet Office)http://www8.cao.go.jp/cstp/kihonkeikaku/index5.html

1Preventive maintenanceof facilities andinfrastructures

Smart factories

IT systems

RobotsElectronic filing

Energymanagement
systems

Electronic
publishing

DronesPaperless offices

Online shopping
Care, healthmanagement

Introduction of IT
into operations

Remoteconferencing

Smartlogistics

Smart appliances,smart homes

Remote medicine,electronic medical
records

Wearable
devices

Telecommuting

Smart farms
Automated driving,
driving support systems

Fig. 2: Increasing number of IoT-based solutions

2

The former Green IT Promotion Council and JEITA Green IT Committee have reviewed the possibility ofvisibly representing the amounts of CO2 emissions reduced using traditional IT solutions, and believe thatthe introduction of IoT will lead to even more reductions and new benefits to the environment.
Through our investigations into a number of examples of IoT utilization in logistics and agriculture, wehave identified, and listed in Table 1, examples from the perspective of social benefits to the environment.
While these results may be achievable to some degree through traditional IT (pre-IoT), we believe that
greater efficiencies can be achieved by gathering big data through IoT, and using AI and other technologies
for analysis and learning to identify optimal implementations, and reflecting the optimal implementation inlogistics and agriculture.
Table 1: Examples of environmental benefits achieved with the use of IoT in logistics and agriculture(1) Energy used for transport can be reduced by using IoT to gather data on travel routes
associated with transport, analyzing this data, carrying out learning and otherprocesses, and using these results to identify optimum travel routes (to reduce travel
distances, avoiding congestions, etc.).
(2) Energy used for transport can be reduced by using IoT to gather big data on theweather, analyzing this data, carrying out learning and other processes, and using theseresults to identify optimum travel means (modal shifts, etc.).
(3) Unneeded excesses and energy used for transport can be reduced by using IoT togather big data on logistics and consumption, analyzing this data, carrying out learningand other processes, and using these results to improve the precision of demand
forecasts.
(4) Unneeded excesses in product supply and disposal can be reduced by using IoT togather big data on logistics and consumption, analyzing this data, carrying out learningand other processes, and using these results to improve the precision of demand
forecasts. (reduction of food product spoilage)(5) The quality of agricultural products can be made more consistent and the amounts ofproducts that are disposed of can be reduced by using IoT to gather data on the growthof crops, analyzing this data, carrying out learning and other processes, and using theseresults to implement advanced management (reduction of food product spoilage).
(6) The ability to maintain the quality of transported items can be improved, and amounts
of items that are disposed of can be reduced by using IoT to gather temperature data
inside frozen transport vehicles, analyzing this data, carrying out learning and otherprocesses, and using these results to implement advanced management (reduction offood product spoilage).

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2.

What are the Green Benefits enabled by IoT solutions?

Benefits to society enabled through the use of IoT are expected to increase in a diverse range of areas.
Conceivable benefits include benefits to environmental conservation as shown in Fig. 3, improvements in
comfort and satisfaction, and the mitigation of societal issues such as labor shortages.
The environment is intrinsically a far-reaching concept and appears to be gradually shifting alongside thechanges in societal needs and the awareness of people. Article 1 of the Basic Environmental Law reads asfollows.
Article 1: The purpose of this law is to comprehensively and systematically promote policies forenvironmental conservation that ensure a healthy and cultured life for both the present and future
generations of the nation, as well as that contribute to the welfare of mankind, through articulating
the basic principles, clarifying the responsibilities of the State, local governments, corporations andcitizens, and prescribing the basic policy considerations for environmental conservation.
As we reflect on IoT's possibilities based on the perspectives noted above, and given how IoT introduction
is rapidly expanding, we can expect to see a variety of benefits in many areas of society. JEITA's Green IT
Committee has evaluated and expressed the benefits of IT in terms of reductions in CO2 emissions. If,
however, there is a possibility of ensuring a "healthy and cultured life for both the present and future
generations of the nation, as well as contributing to the welfare of mankind" through the use of IoT, what
are the appropriate methods for evaluating and expressing these benefits?
 Perspectives centered around the environment
In this report, we attempt to quantify the degree of benefit to straightforward areas that can be consideredwithin the environmental perspective, and lead to CO2 emissions reductions. In other words, the scope of
this review in as far as calculating CO2 emissions reductions based on a number of examples was defined
to be the area enclosed in the red box in Fig. 3 . Calculations were done using the method developed by theGreen IT Promotion Council3. (See Table 2 below.)
Of the items enclosed in the red box, while the Green IT Promotion Council did not include the "reductionof food product spoilage" in its calculations of potential CO2 emissions reductions from IT solutions(pre-IoT), this has been included in the current calculation as significant benefits can be expected in thisarea through the use of IoT. (See “4. Results from Trial Evaluations of Green Benefits”.)
 Perspectives that derive from the environmental perspective
The range of images elicited by the word "environment" appears to have expanded in recent years. Even
the word "adaptation," which refers to actions in response to a variety of phenomenon that can be attributed
to climate change, is occasionally discussed within the framework of the environment. Energy conservation
is no longer the only proposition for the smartification of buildings and roof areas, which are increasingly
being presented as a means of "smart wellness" that includes comfort and health benefits.

3

The Green IT Promotion Council (fiscal 2008-2012) was founded under the Ministry of Economy, Trade and Industry's
Green IT Initiative with a membership of seven IT related organizations. The Council carried out a variety of projects inJapan and overseas for a period of five years to promote IT and energy conservation in societies through IT.
http://home.jeita.or.jp/greenit-pc/about/index.html

4

We expect that these benefits can be developed even further through the use of IoT.
With regards to the benefits of IoT solutions, in this report, we explore "CO2 emission reductions" from the“environmental perspective” as our first item of review. Moving forward, we intend to gradually extend theideas associated with the “environment” to “adaptation” and “smart wellness,” and evaluate and express
these areas as "Green Benefits." Depending on the specifics of each area of benefit, there may be cases
where these benefits may require additional methods of evaluation and expression that may not necessarily
correspond with or can be converted to the equivalent of CO2 emissions. Indeed, discussions in this veinhave already been raised in the process of compiling this report.
(See "4.3 What we have discovered in these trial evaluations".)

Reduce the amount of energy used for transport
through:
Optimized travel routes;
Advanced demand forecasts, and;The use of sensors to reduce patrolling work, etc.
Reduce air-conditioning through advanced
temperature control.
Reduce lighting through advanced personnel traffic
management.

Reduce personnel through the useof sensors.
(Reduce office space.)

Achieve on-time delivery
through the optimization of
travel routes.

Reduce food product spoilage through:Advanced crop growth management;
Advanced temperature control duringtransport, and;Reduce labor shortages throughthe use of sensors (agriculture,transport).

Advanced demand forecasts, etc.
Improve comfort levels withadvanced temperature control.
- Improve nutritional content- Resolve crop shortages by stabilizing harvest
volumes through:Advanced crop growth management.

Easy to grasp as the environmental viewpoint

Unfamiliar as the environmental viewpoint(Improved customer satisfaction or comfort, solutions to difficult problems, etc.)

Easy to convert to CO2

Hard to convert to CO2

Fig. 3: Benefits enabled by IoT utilization, and areas that were reviewed for this report (in the red box)

5

3.

Evaluation of Green Benefits enabled by IoT solutions: -- Asenvisioned in this report

3.1

Evaluation methods

In this report, we have calculated CO2 emissions reductions for a number of examples of IoT solutions.
Calculations were done using method developed by the Green IT Promotion Council (Table 2). Moving
forward, we hope to expand our scope of evaluations of "Green Benefits" achieved through IoT solutions
as shown in Fig. 3. In doing so, not all cases may be readily convertible to CO2 emissions, but there are anumber of cases where this can be done with some ingenuity, such as the "reduction of food productspoilage."In this report, we seek to evaluate and express those that can be considered straightforward from anenvironmental perspective and converted into CO2 emissions. (See 4. “Results from Trial Evaluations ofGreen Benefits”.)
Table 2: Components of the effects of IT solutions, and formulae for their calculation developed by
the Green IT Promotion Council (fiscal 2008-2012)
Components(1) Consumption ofmaterial(2) Amount traveled bypersons(3) Amount traveled by
items(4) Office space(5) Warehouse space

Component subjects
Paper, CDs, books, etc.
Aircraft, automobiles,
trains, etc.
Trucks, railroad, cargo,etc.
Space occupied bypersons (including work
efficiency), space
occupied by IT
equipment, etc.
Warehouses, refrigerated
warehouses, etc.

Component calculation formulae
Reduction in consumption of material x Primary unit ofconsumption of material
Reduction in personal travel distance x Primary unit of travel
Reduction in item travel distance x Primary unit of travel
Space reduction x Primary unit of energy consumption per space* Space reduction equals the number of persons reduced
multiplied by the space occupied per person, or the number ofpieces of equipment reduced multiplied by the space occupied
per piece of equipment.
Space reduction x Primary unit of energy consumption per space

Amount of change in power consumption x Primary unit of gridpower(6) Electricity and energy* This applies when converting electrical power into CO2
Power consumed byconsumption (IT andemissions.
servers, PCs, etc.
network equipment)* This represents the amount of energy consumed from the use of
IT equipment, and does not include energy consumed for the
manufacture or disposal of such equipment.
Amount of change in data communication volume x Primary(7) Network data
unit associated with data communication
Network data
communication*
The
amount of energy consumed for network communications
communication volume
volumeincludes energy consumed for Internet communications, but not
intranet communications.
Amount of change in activity x Primary unit of the amount of
Activities other than the(8) OtherabovechangeSource: Green IT Promotion Council, Feb. 2013, "Energy-Saving Benefits from IT Solutions for Society as a Whole-- Perspective on Evaluations of Benefits from 'Green by IT' -- Explanatory booklet"
http://home.jeita.or.jp/greenit-pc/activity/reporting/110628/pdf/survey02.pdf

6

3.2

Items to be included in the evaluation

Listed below are the subjects that are calculated in this report as "Green Benefits" enabled by IoT solutions.
(1) Items that represented benefits enabled by IoT solutions and were the subject of calculation
for their contribution to CO2 emissions reduction by Green IT Promotion Council were
included as before.
[Ex 1] Reductions in the consumption of material[Ex 2] Reductions in the amount traveled by persons, and other items that can be evaluated in theframework of Table 2 above.
(2) Items that represented benefits enabled by IoT solutions but were not the subject of
calculation for their contribution to CO2 emissions reduction by Green IT Promotion
Council were included in the evaluation if one of the following applied.
[a] Is a benefit that was achieved with IT solutions (pre-IoT) and was not converted into CO2emissions by the former Council, but whose degree of benefit will significantly grow throughthe use of IoT.
[b] Is a benefit that was not conceivable with IT solutions (pre-IoT) but will be newly createdthrough the use of IoT.
For example, items included in Table 1 "Examples of environmental benefits achieved with the use of IoT
in logistics and agriculture" fall into this category.

7

3.3

Areas evaluated (logistics and agriculture)

3.3.1

Reasons for choosing logistics for evaluation

In order to improve the efficiency of logistics―the foundation of the country's economy and lives of its
citizens―the General Outline of Logistics Initiatives of 2013-2017 pushed forward with initiatives aimedat (1) achieving efficient logistics that support industrial activities and the lives of the country's citizens, (2)further reducing environmental footprints, and (3) ensuring safety and peace of mind in logistics. Activities
relating to Green Benefits ((2) further reducing environmental footprints) were also seen as important andplaying a major role. Specific subjects included implementing enhancements to transport capabilities inrailroad and coastal shipping, promoting modal shifts, reducing the energy consumption of trucks, ships,
trains, etc., and promoting joint transportation and delivery through collaboration between cargo owners
and logistics operators.
In the subsequent Outline (2018-2022), discussions included background factors that affect the area of
logistics, including the declining population resulted from dwindling birthrates and an aging population;technological developments such as IoT, automated driving, and drones; further growth of the e-commercemarket; growth in the Asian region; risks of earthquakes, typhoons or other disasters; and implementationof the Paris Agreement. Due to these factors, it is expected that far-reaching policy actions will be requiredin logistics, including productivity improvements and overseas expansion, improved robustness of logistics
networks, and the formulation of environmental policies. Of these, "technological developments such as
IoT, automated driving, and drones" and the "implementation of the Paris Agreement" in particular are in
direct accordance with the aims of the reviews carried out by the Green CPS/IoT Review Working Group.
The logistics area was spotlighted as an example in this report as further consideration of these factors haveled us to believe that Green Benefits that utilize IoT in logistics will become important moving forward forboth society and policy.
Developments within the next 5-10 years, and directions in actions to take<1> Declining population caused by dwindling birthrates
and an aging population- The population of Japan will dip below 120 million by 2026 (6 million
decrease from 2016), and the percentage of aged people will exceed
30% of the population in 2024.

<2> Technological developments such as IoT, automateddriving, and drones- IoT will add an economic value of roughly 2.3 trillion dollars in the areas of
distribution, retail, and logistics over the 10-year period from 2013 to 2023.

<3> Further growth of the e-commerce market-

The e-commerce market for the sale of goods has enjoyed
year-on-year growth of 10% or greater every year from 2010 to2014. And, while growth for 2015 was slightly muted at 7%, themarket continues on a growing trend.

<Directions in actions to take 1>
Improve productivity and secure
enough human resources.

<Directions in actions to take 2>
Improve productivity through the use oftechnologies such as IoT, automated driving,and drones.

<Directions in actions to take 3>
Improve productivity in ways that
address diversifying markets.

<Directions in actions to take 4>- Establish logistics systems that help
Japanese companies to operate overseas.
- Transfer logistics systems that are used inJapan to overseas.

<4> Growth in the Asian region- The GDP of the ASEAN10 will double by roughly 2025 (4 trillion
dollars above 2015 levels) and surpass that of Japan.

<5> Risks of earthquakes, typhoons or other disasters- There is a 70% risk of a magnitude 7.0 class earthquake striking directly
beneath the Tokyo metropolitan area in the next 30 years<6> Implementation of the Paris Agreement- Japan has pledged to reduce greenhouse gas emissions by 26% of2013 levels by 2030.
- The transport sector must reduce 62 million tons of CO2 emissions byfiscal 2030.

<Directions in actions to take 5>- Increase the robustness oflogistics networks.
<Directions in actions to take 6>
Implement environmental
countermeasures through inter-corporate
partnerships and utilization of technology.

Fig. 4: Shifts in the state of affairs and key initiatives following the formulation of the current
Outline of Logistics Initiatives
Source: Ministry of Land, Infrastructure, Transport and Tourism (Dec. 2016)8

3.3.2

Reasons for choosing agriculture for evaluation

The utilization of IT has been ongoing in the agricultural sector for some time. Meanwhile, the "Japan
Revitalization Strategy 2016 -- In Approaching the Fourth Industrial Revolution 4" announced by thegovernment in June 2016 cites a number of key initiatives including the "Fourth Industrial Revolution,""Overcoming environmental and energy constraints, and increasing investments," and "Proactive
development of agriculture, forestry and fisheries, and strengthening their export capabilities." The strategy
states the need for “making fundamental productivity improvements through the utilization of innovativetechnologies” and other new initiatives to enable the "Proactive development of agriculture, forestry andfisheries, and strengthening their export capabilities", and cites the promotion of IoT and big datautilization, as well as the utilization of AI (artificial intelligence) as specific initiatives.
Furthermore, the Ministry of Agriculture, Forestry and Fisheries' "Study Group on Achieving Smart
Agriculture" is advancing ideas that can be enabled by the fourth industrial revolution such as
ultra-labor-saving agriculture, and strategic production through extensive use of data as shown in Fig. 8.
The agricultural sector was spotlighted as an example based on these factors.

Ultra-labor-saving agriculture with robotics and automation

Strategic production through the extensive use of data
Big weather dataand other factors

Real data from the field- Disease xx may proliferatein two weeks. Spraying ofyy agent recommended.

- Increase fertilizing volume infield A by 3%.
- Field B requires no fertilizing.

 Achieve dramatic labor-savings and safe
working conditions through automateddriving of agricultural machinery.

 Big data obtained from sensors
and such are analyzed to present the
optimum methods of growth
management for each field.

 Automate harvesting and other workthat previously relied on human labor, andachieve 24-hour operation by enablingnighttime operations.

 Risk forecasts are based on big
weather data and other factors, inorder to implement countermeasuresahead of time.

Big data enables forecasts and better productivity.

Complex work can be performed by robots or automation.
Robotics

Big data

Fourth Industrial
Revolution

AI

Collaboration and greater efficiency between production, distribution,and sales

Make agricultural work accessible to all.

Retailer
ProducerThis is xx
disease. Please
perform yy.

Knowledge iscodified using
AI.

Wholesaler
Platform for handlingvarious forms of dataNew
farmers

Restaurant
industry

Consumer

Share information on
operations and load conditions.

Food producer

 Use image analysis to enable early Create systems for recording the techniques
detection of symptoms caused by pests, and and decisions made by innovative farmers,present optimum methods of control.
compiling this information as data, and sharing
this know-how with new farmers, etc.

 Ascertain market trends and the needs of Share information on the operation of
actual users and consumers in a timely manner trucks and other elements across boundaries
to produce agricultural products in ways thatof product categories and industry to reducerising transportation costs.
meet these needs.

Tacit knowledge of production sites can be visually represented.

Various forms of information connect with each other to create newvalue.

Fig. 5: Possibilities of AI and IoT utilization in agriculture (schematic)
Source: Ministry of Agriculture, Forestry and Fisheries, Fifth Study Group on Achieving Smart
Agriculture (Nov. 2016)

4

Japan Revitalization Strategy 2016 -- In Approaching the Fourth Industrial Revolution –http://www.kantei.go.jp/jp/singi/keizaisaisei/pdf/2016_zentaihombun.pdf

9

4.

Results from Trial Evaluations of Green Benefits -- Examples in
Logistics and Agriculture --4.1

IoT utilization in logistics

4.1.1

Status of IoT utilization

Logistics is an area where IoT utilization is expected to move forward. With the popularization of
e-commerce, delivery lots have shrunk while shipment frequencies have grown. And, with the working age
population shrinking, shortages of delivery truck drivers and workers at logistics depots are becoming an
issue. Also considered an issue is the increase in the frequency of re-deliveries due to households having
fewer members and shifting lifestyles. Moreover, owing to the growing convenience of e-commerce andother factors, requirements in the area of delivery quality, such as delivery timeframes and temperature
control during delivery, are intensifying.
Given these conditions, IoT is being utilized in a broad range of areas of logistics including delivery,storage, and cargo handling (Figs. 6, 7, and 8). One area that can be cited as an example is logistics
planning. Systems are now in place for monitoring truck location, operating status, and driver conditions
for the purpose of truck dispatch planning, driver management, and delivery planning. Cargo is also being
tracked with the use of electronic tags attached to pallets, cargo, or vehicles, and methods for visually
representing cargo and workers are being employed at warehouses and depots to improve working
efficiency. Warehouses and depots are also introducing automatic sorters, automatic transporters, andpicking robots to save on labor and improve efficiency. Verifications are also underway on enhancing theconvenience of the "last mile" by using receiving boxes that are linked up with smartphones, as well as forthe practical implementation of real-time delivery services using self-driving vehicles or drones.
These various forms of IoT utilization also contribute in a number of ways to the reduction of CO2
emissions in logistics. While efforts have been underway to use IT to reduce CO2 emissions, we believethat these efforts are progressing into new approaches that utilize IoT.

Environment

LogisticsSmaller delivery lots, higher frequency Resolve  Reduce energy consumption.
bothMore rigorous requirements for Reduce waste material.
delivery quality
 Avoid increasing environmental(Timeframes, temperature control,
footprint when introducing new
etc.)services.
Worker shortage (Truck drivers,
workers at logistics facilities, etc.)
Means for realization

Means for realization

IoT
 Visually represent the state ofobjects using IoT solutions.
 Analyze huge amounts of data.
 Methods for realizing greater
efficiency and new services.

Fig. 6: Relationship between logistics, IoT, and the environment

10

Individual cargo management
Visual representation of work using
location sensors
Automated warehouses (picking
robots, automated transport robots,sorters)
Work instructions using virtual and/or
augmented reality (VR/AR)

Monitor vehicle locations andoperating statuses, Dispatch
planning, Driver management.
Automated driving *ADAS

Truck driver shortage
Increasing cargo volume

Improve facility rotation rates
Maintain merchandise quality
Strictly observe delivery
timeframesWorker shortage

Depot or logistics
warehouse

Depot or logistics
warehouse
Drones,self-driving
vehiclesShare common
logisticalinformation

Big data

Analysis,machine learning,
security, etc.

Optimizetransport plans

Tag-based cargo
tracking (constanttemperature control)

Cloud

Automatic
inventoryordering

Receiving
boxes

Farms

Factories,
wholesalers

Stores andhomes

Order-directed production(make-to-order),Customization

Smaller delivery lots, higher
frequency, more re-deliveries

Fig. 7: Areas in logistics where IoT is being utilized

11

Monitoring by IoT

Data gathering

Analysis andoptimization(utilize AI)

Health condition

DriverPlanning

Location, operating
status Optimize
transport plan Dispatch plan Joint delivery
 Safe driving

Big data
Inventory, truck
arrival status
Inventory, demand
forecasts

Traceability

Cargo ID, location,
temperature Quality verification

Cargo

Cargo

Big data

 Optimize
transport plan

Freezer/Refrigerator
temperature

Warehouse work

(pharmaceuticals,
food products,
etc.)

WorkerWorker andcargo
movement

Big data

Cargo

Fig. 8: Examples of utilization of big data gathered through IoT

12

 Work instructions
 Greater work
procedure
efficiency

4.1.2

Results from evaluations (trials) of specific examples

Trial evaluations were carried out for a number of examples to determine how CO2 emissions were being
reduced in logistics with the utilization of IoT. Calculations for quantification were carried out using
calculations developed by the Green IT Promotion Council (Table 2). The document below was also
referenced for CO2 conversion indices and other values.
 Document"Energy-Saving Contributions to Society from IT Solutions -- Perspective on Evaluations ofContributions from 'Greening by IT' -- Explanatory Booklet"
Green IT Promotion Council, Feb. 2013,
http://home.jeita.or.jp/greenit-pc/activity/reporting/110628/pdf/survey02.pdf
 Calculation methods
Table 2 (re-listed from above): Components of the effects of IT solutions, and formulae for theircalculation developed by the Green IT Promotion Council (fiscal 2008-2012)
Components(1) Consumption ofmaterial(2) Amount traveled bypersons(3) Amount traveled by
items(4) Office space(5) Warehouse space

Component subjects
Paper, CDs, books, etc.
Aircraft, automobiles,
trains, etc.
Trucks, railroad, cargo,etc.
Space occupied bypersons (including work
efficiency), space
occupied by IT
equipment, etc.
Warehouses, refrigerated
warehouses, etc.

(6) Electricity and energy
consumption (IT andnetwork equipment)

Power consumed byservers, PCs, etc.

(7) Network data
communication
volumeNetwork data
communication volume(8) Other

Activities other than theabove

Component calculation formulae
Reduction in consumption of material x Primary unit ofconsumption of material
Reduction in personal travel distance x Primary unit of travel
Reduction in item travel distance x Primary unit of travel
Space reduction x Primary unit of energy consumption per space* Space reduction equals the number of persons reduced
multiplied by the space occupied per person, or the number ofpieces of equipment reduced multiplied by the space occupied
per piece of equipment.
Space reduction x Primary unit of energy consumption per space
Amount of change in power consumption x Primary unit of gridpower
* This applies when converting electrical power into CO2emissions.
* This represents the amount of energy consumed from the use of
IT equipment, and does not include energy consumed for the
manufacture or disposal of such equipment.
Amount of change in data communication volume x Primary
unit associated with data communication* The amount of energy consumed for network communications
includes energy consumed for Internet communications, but not
intranet communications.
Amount of change in activity x Primary unit of the amount ofchange13

[Case 1] Forklift traffic line analysis service
 Overview and key points from an IoT perspective
 Forklift location information acquired by autonomous locating devices (sensors), IMES, andcameras are analyzed in the cloud to achieve work optimization and improve operations.
- Optimize warehouse entry and exit routes, and determine better inventory locations.
- Utilize basic data for reviewing logistics center layout and optimum number of forklifts.
 Key points from an IoT perspective
 Gather and capture detailed traffic line data through locational corrections made byautonomous locating devices (sensors) and cameras.
 Categorize work patterns by linking them with work information, thereby detecting unneededexcesses.
 Improve efficiency of inventory locations by linking them with cargo data.
 Calculation of effects, and trial results:[Scope of calculations] Enumerations were hypothesized for each calculation component for the effects ofimplementation at one refrigerator/freezer warehouse.
[Calculation conditions] Distances were inferred from forklift operating times and average speed. As these were
electrically operated, the primary unit was deemed to be that of electric vehicles.
 It was supposed that an autonomous locating device (sensor) was attached to each forklift. The
primary unit was deemed to be that of mobile communication devices.
[Points of the evaluation]
 Upon categorizing the large amounts of data into "required," "accessory," and "no-value" forklift
traffic line patterns, appropriate entry and exit routes were identified based on more appropriate
inventory locations. Effectiveness measurements of operational improvements were done fromthe perspective of CO2 emissions reductions.
[Calculation results] Suppression of CO2 emissions: 13% (approx. 2.2 t-CO2/year)
Beforeimplementation

Components

After
implementation(3) Amount traveled byitems

Number of forklifts: 10  9Distance traveled daily: 7 km x 5
hrs
Days operated yearly: 200

4.973

3.580

(4) Office space

Driver's office: 12  11 persons

11.94710.952(6) Electricity andenergy consumption

Number of autonomous locating
devices: 0  9
Laptop PCs: 0  1
Cloud

0.000
0.000
0.000

0.013
0.028
0.143

0.000

0.001

(7) Network data
communication
volumeRemarks
Distance was inferred from forklift
operating hours.
Daily average speed was presumed tobe 7 km/h.
The primary unit was deemed to be thatof an electric vehicle.
The primary unit was deemed to be thatof a mobile communication device.
Used for management purposes
Derived from CFP verification locations

Suppression of CO2
emissions: 13%(2.2 t-CO2)

Beforeimplementation

Reductions(suppression)

14

Increases

After
implementation

[Discussion]
In addition to reducing CO2 emissions, we believe that significant contributions can be madewith regard to addressing labor shortages in the logistics industry. In other words, logisticalquality can be ensured from the fact that the same amount of work can be efficiently undertaken
by fewer numbers of people, as well as from the analysis of large amounts of data gathered withthis solution to visually represent the knowledge assets held by experienced drivers so that they
can be shared with new drivers for navigation.
 Before implementation
Location map
ロケーション
MAP Cargo is retrieved and placed
based on experience andon-the-fly decisions made by
forklift drivers.

AB

 This creates variances in work
procedures and forklift traffic lines(efficiency) between experienced
and new drivers.

C After implementation
 By analyzing the causes of variances in work procedures and forklift traffic lines(efficiency) between experienced and new drivers, improvements were made to entry andexit routes and inventory locations to reduce unneeded forklift movement.
Schematic of work performance management
Camera[Data] Vehicle no., worker ID, date/time, location, direction, distance, etc.

 Movement
information
 Location
information

Gather work
performance

Work instructions no.

Worker ID

Vehicle no. Driving timeWork traffic line

Location map

Improved

Schematic of traffic analysis
Efficient entry and exit routes

Efficient inventory locations

Location map

Location map

15

Distance driven

[Case 2] Visual representation of transport quality and analysis
services using sensor logger Overview and key points from an IoT perspective
 Sensor loggers mounted with several types of sensors (for temperature, etc.) were placedin the packaging of items being transported as well as in the transport media to gather andvisually represent the acquired transport quality data.
 Data acquired on the sensor logger was analyzed by collating it with driving data and otherdata to visually represent and analyze transport quality.
 Transport plans can be improved and operations can be made more efficient by utilizing
results from the analysis.
 Calculation of effects, and trial results:[Scope of calculations] Cargo consisting of processed food products were transported to two destinations onrefrigerator trucks.
 Cargo to be shipped to these destinations was managed by control clerks at the depot.
 Soiling or damage to products occurred during transport.
 Power consumed at the data center for data analysis was not included in calculations.
[Calculation conditions]
Delivery frequency: Once daily / Amount of cargo per destination: 1 ton / Warehouse operating
hours: 8 hours daily / Cargo control clerk work hours for this work: 2 hours daily / Annual
operating days: 365 days / Data gathering: Once daily / Sensor logger's data communication
volume: 4 Mbytes per delivery / The rate of occurrence of soiled or damaged products was
defined to be 0.004%* of the transported volume, all of which were to be disposed of(*Calculated by JEITA based on documentation published by logistics industry organizations
and other organizations).
[Points of the evaluation]
 Optimization of transport processes, including route distance reductions, while enabling
improvements to transport quality.
 Efficiency improvements and other effects stemming from IT solution implementation, as
well as effects relating to IoT utilization targets (transport quality) were considered: Soiled
or damaged products = Reduce product disposals[Calculation results] Suppression of CO2 emissions: 38% (approx. 2.2 t-CO2/year)
Components(*Enter amounts for before and after implementation)

Beforeimplementation
t-CO2/year

After
implementation
t-CO2/year

Remarks(3) Amount traveled byitems- Number of refrigerator trucks: 2 → 1- Distance traveled daily: 30 km → 23 km- Annual operating days: 365 → 365- Delivery (cargo) amounts: 1 ton per delivery
per truck → 1 ton per delivery per truck

5.4

3.4

(4) Office space- Number of cargo control clerks: 2 → 1

0.50

0.25

- Two delivery destinations- Cargo content is different for each delivery
destination.
- Delivery routes will be optimized after
implementation.
- After implementation, one control clerk will be able
to manage cargo for both locations.

(6) Electricity and energy
consumption(7) Network data
communication
volume- Laptop PCs used: 2 → 1- One PC used by each cargo control clerk.

0.007

0

0.0037- Amount of soiled or damaged products =Amount of products disposed of: 8g perdelivery → 4g per delivery.

0.081

0.040

- One logger is placed in each refrigerator truck.
- Data is gathered once upon the completion of one
delivery.
- One ton of cargo is delivered per delivery
destination.
- The rate of occurrence of soiled or damaged
products is 0.004%* of the transported volume.
- All soiled or damaged products were to bedisposed of, and it is presumed that the rate ofoccurrence of soiled or damaged products will be
reduced by 50% after implementation.

Suppression of CO2
emissions: 38%(approx. 2.2 t-CO2/year)
t-CO2/year(8) Other

0.014- Sensor logger data volume: 0 Mbyte perdelivery (not used) → 4 Mbyte per delivery

Beforeimplementation

Reductions(suppression)

16

Increases

After implementation

[Discussion]
 Calculations show that the optimization of transport routes for "Volume of items transported =Refrigerator trucks" has the largest CO2 emissions suppression effect (approx. 2t-CO2/year) .
As such, the implementation of solutions to logistics processes can be expected to contributeto the suppression of CO2 emissions in this industry.
 The extent of electrical power consumed by servers to process data gathered by the sensor
loggers could not be identified or estimated (To be addressed moving forward).
 In evaluations of the effects of IoT implementation, the above can be categorized under the"(8) Other" component as described in calculations of the amounts of emissions being
suppressed from the use of IT solutions.
 Before implementation

Distance = X

Cargo control clerk 1

Processed
food
products

Base

Cargo A

Deliverydestination A

Cargo B
Cargo control clerk 2Distance = Y
Soiled or damaged products
from cargos A and B =DisposeDeliverydestination B After implementation
Cargo A+B
LoggerDistance = X
LoggerDeliverydestination A
LoggerCargo control clerk 1
Cargo B
Base

Processed
food
products

Soiled or damaged products
from cargos A and B =DisposeDistance = Z

Distance = Y
Deliverydestination B

Logger17

[Case 3] Remote temperature control solution forfreezer/refrigerator transport vehicles
 Overview and key points from an IoT perspective
 Sensors for temperature control and an Internet communication device are installed on
freezer/refrigerator trucks. During transport, temperature and location information are
periodically sent to the control center to gather, store, and manage information on thetemperature inside the trucks.
 The control center periodically monitors information on the temperature inside the trucks,
and sends temperature control information to achieve the appropriate temperature to enable
the transport of food products at the temperatures specified for different food products andensure the quality of food products being transported.
 The gathered information is also used to prepare reports on temperatures during transporten-route to customers and to identify the causes of any problems that may occur duringtransport.
 Calculation of effects, and trial results:[Scope of calculations]
Before implementation: The scope of operations is to transport refrigerated food products(meat) at a constant temperature inside the truck during transport.
After implementation: The scope of operations is to transport refrigerated food products (meat)by monitoring and controlling temperatures inside the truck via communications between
the truck and control center.
[Calculation conditions]
Refrigerated food products (meat) are transported from Tokyo to Yokohama (31.6 km) in arefrigerated 2-ton truck once a day, 250 days a year. The mass of refrigerated food products(meat) to be delivered to customers is one ton per day.
[Points of the evaluation]
The implementation of this solution reduces food product (meat) waste, and the evaluations(effects) of this aspect was evaluated as the lifecycle CO2 emission of meat (primaryenvironmental footprint).
[Calculation results] Suppression of CO2 emissions: 0.72% (approx. 30 t-CO2/year) (t-CO2/year)
BeforeAfter
implementat implementat
ion
ion

Components(3) Amount traveledby items

Volume transported (*1): 1t per delivery
Transport frequency: Once daily
250 days per year
Transport distance: 31.6km(6) Electricity andenergyconsumption

1.63

1.62

Power consumed by data center equipment
Power consumed by facilities

0

0.13

(7) Network data
communication
volumeTemperature data: 1KB/delivery (send)
Temperature control data: 1KB/delivery (receive)
Number of transmissions: Once a minute
Duration of usage: 38 minutes

0

0.00003(8) Other

Environmental footprint of food product beingtransported

4,130

4,100

Remarks*1: The mass delivered to customers constitutes thevolume transported.
1.006 tons were transported, taking into account thepre-implementation amounts that were being disposedof (insufficient temperature control).

Amounts disposed of during transport (percentage):
Proportion relative to initial production volumes of meat inadvanced industrial regions of Asia: Approx. 6%
Amounts (percentage) disposed of due to insufficienttemperature control during transport was hypothesized tobe 10%.

t-CO2/year

Suppression of CO2
emissions: 0.72%(approx. 30 t-CO2/year)

Before implementation

Reductions(suppression)

Increases

18

After implementation

[Discussion] While "food product waste during transport" was expressed in terms of CO2 emissions
reductions in this evaluation, we intend to review other ways of expression that are more
readily communicable.
 In this example of solution implementation, the reduction of "environmental footprints byIT" (reduction of food product [meat] waste) exceeded the increase in "environmental
footprints of IT." As such, we can expect to see effects in reducing the environmental
footprint.
 With regard to power and energy consumption at the control center, detailed calculations
were made for individual servers and storage devices that make up the data center forevaluation. As future evaluations for IT/IoT solutions will include solutions that arefounded on data centers similar to this control center, we believe that one of the issues
moving forward will be to develop methods for evaluating data centers as single entities.
 Before implementation

Temperature inside the truck is set to a constanttemperature for transport.

 After implementation

Temperature
information
Temperature
control
information

Control center Temperature information is sent to the control center via the Internet to control the temperatureduring transport.
 If abnormal temperatures are detected, temperature control information is sent to the truck tocontrol the temperature.

19

Special
Contribution

The following examples of utilizing big weather data as a tool to reduce
CO2 emissions in logistics were contributed by the Japan Weather
Association.

[Case 4] Demand forecasts and energy-saving logistics operations
through the use of big weather data Overview and effects of the solution
 6.32 million tons of food products are being wasted in Japan, and this exceeds the 3.2
million tons of global food aid by a large margin. In this project, we worked towards
reducing food waste by making food product demand forecasts based on the latest weather
forecast data for not only Japan, but also for Europe, and to carry out production operations
in a precise manner based on these demand forecasts. We also achieved modal shifts thatutilize these demand forecasts.
 In addition to demand forecasts, there are many areas where CO2 emissions reductions
and energy conservation can be achieved through the use of big weather data. We expect
to see the use of big weather data in a variety of forms of business.
(1) Quantification of relationships between weather and food product demand
By analyzing the relationships between the weather and demand for specific food products, wehave established methods for forecasting food product demand based on weather. For
analysis, various weather parameters were converted to sensory temperatures and othermetrics that demand is sensitively affected by. We also employed the latest AI technology for
analysis.
(2) Reduction of waste of products delivered daily
The demand for "yose dofu" rises rapidly during the summer seasons, and due to the fact thatits production requires two days and retailers place their orders only one day in advance,
producers have had to guesstimate the actual demand, which in turn has led to the disposal oflarge amounts of products. In this project, we were able to reduce the amount of products
disposed of by approximately 30% through operations that were based on demand forecasts
provided in this project.
(3) Reduction of waste of seasonal products
Sales of "soup for hiyashi chuka (cold Chinese noodles)" drops precipitously after the peak of
the summer heat. In the past, operations to clear these products before the autumn season
had been a major issue, forcing operators to dispose of large amounts of product in
September. In this project, by improving the precision of demand forecasts for the
post-summer season, we were able to reduce the amount of products disposed of by nearly20%.
(4) Achieving modal shifts that make use of demand forecasts
To date, the delivery of "coffee in PET bottles" involved the use of one-week weather forecasts
to identify demand in different areas, based on which the products were transported by trucks.
In this project, we used a high-precision two-week demand forecast and moved towards
shifting the transportation mode to ships given the longer lead-time to delivery. And, for theships, we implemented a system that would recommend optimal shipping routes based on
weather and hydrographic conditions to reduce fuel consumption while ensuring on-time
operations. As a result of these efforts, we were able to reduce CO2 emissions by roughly 48%
over a period of sixth months (*).
 This project was awarded the three awards below in fiscal 2016.
 17th Grand Award for Logistics Environment, Grand Award for Logistics Environment
 Energy Conservation Grand Prize for Fiscal 2016, Minister of Economy, Trade and Industry
Award (Business Sector)
 - 4th Food Industry Mottainai Awards, Ministry of Agriculture, Forestry and Fisheries Food
Industry Affairs Bureau Chief Award(*) Values as calculated by the Japan Weather Association.

20

Special
Contribution

Transforming logistics with weather forecasts
Amount ofFood wasted inJapan

Roughly 3.2
million tons

Roughly 6.32
million tons

Food waste in Japan amounts to roughly twice the amount of global global food aid
food aid.
Not only is this situation wasteful, the disposal and return of theseproducts have generated unneeded excesses of CO2 emissions.
We intend to create a world with no unneeded excesses andtransform the commonly held notions about logistics throughSource: "Approaches for Reducing Food Waste" (Ministryof Agriculture, Forestry and Fisheries)

state-of-the-art technologies that leverage weather forecasts and AI.
Food waste was reduced with the use of refineddemand forecasts Preparedthat forutilize
weather information.
Sagamiya

Announced on Wed., Jun.JWA Special Weather
Forecast
22

Shokuryo, Yose Dofu

Improve CO2 reduction effects through collaboration in
the supply chain

Amount of wasted tofu
(Sagamiya Shokuryo)

Truck transport

Reduction of roughly 30%

Large environmental
footprint
Driver shortages

The tofu index, which incorporates weather
information, was independently calculated torefine the demand forecast. Operations weremodified based on this forecast to successfully
reduce food waste.

Reduceenvironmental
footprint
Reduce labor costs

Ship transport

Reduction of roughly 20%

CO2 reduced by roughly 48%
A two-week weather forecast was prepared using
weather data from an overseas weatherorganization (ECMWF*). Quicker decision-making
enabled a modal shift, leading to a successful
reduction in CO2 emissions.
* Modal shift: To shift modes of transportation fromtrucks to freight trains or ships from thestandpoint of CO2 emissions reduction and better
logistics efficiency.

Modal shift

Amount of wasted soup for hiyashi
chuka (cold Chinese noodles) (Mizkan)

Modal shift*(Nestle Japan, Kawasaki Kinkai Kisen
Kaisha, Ltd.)

Reduce environmental footprint andimprove transport efficiency
Moving forward with this modal shift helps toalleviating driver shortages and long-distancedriving. Also, fuel consumption can be reduced byrecommending optimal routes for ships based on
weather and marine current information.

Managing the "final production volume" iscritical for seasonal products with relatively
long "best by" dates. Given this, sensory
temperatures were calculated from different
factors, such as temperature trends leading up
to the previous day, to refine demand forecasts
and successfully reduce food waste.

Supply chain vision to realize
Manufacturers
Raw materials
Previouschallenge
1

Wholesale,
distribution

Limits to empirical demand
forecasts
Global warming is causing
weather phenomena that wehave never experienced.

Previouschallenge
2

Japan Weather Association
Demand forecast information

Demand forecast mismatches
Demand forecasts were being
made independently by
manufacturers, distributors, andsellers with no sharing of
information.

Utilization of weather
information
Make demand forecasts based on
weather information, whose precision
has improved, and weather forecaststhat now extend to two weeks.

Consumers

RetailUtilization of AI and SNS

Information sharing

Improve forecast precision through machinelearning and clustering, and predict consumer
behavior from SNS.

Manufacturers, distributors, andsellers share demand forecast
information that factors in the weather
are taken into consideration.

Setup for transforming logistics through the use of weather forecasts
Futureforecasts

Big data analysis
Weather information
Temperature, weather,rain, wind, temperature
forecasts, sensory
temperature, etc.

Weather at a particulartime, in a particularlocation

Weather slightly
later in time, at thatlocation

Weather-indu
ced moodchanges

SNS data

People's moods at

Tweets that include
location informationthat time, in thatlocation

POS data

Product demand

People's moods
slightly later in time,at that location

Results

Weather
forecasting
technologies
Meteorologicalknow-how
Ensemble forecasting
technologies
Big data analysis, etc.

AI technologies
Product sales,
product master, shop
information, etc.

at that time, in thatlocation

Product demandslightly later in time,at that location

21

Machine learning
Natural language
processing
Pattern recognition
Clustering, etc.

Improve demand
forecasts
Refine demand forecasts
for products that have aclose relationship withthe weather.

Create value
Relationship between
weather and productsales
Differences in sales
peculiarities for different
seasons
Products requiring
special attention moving
forward, etc.

4.2

IoT utilization in agriculture

4.2.1

Status of IoT utilization

Within the "production - distribution - consumption" supply chain in agriculture, we focused on the
production stage. While a broad range of discussions have been carried out to date by relevant government
agencies and other organizations on the topic of promoting IT utilization in agriculture, we expect the useof IoT will bring about further advancements. Based on reviews of several examples in which IoT will be(or can be expected to be) actively employed―reviews that were carried out in the context of its utilization
in information gathering, analysis, and feedback to the field―the overall picture can be summarized, forexample, as shown in the table below. This table was created by referencing the “Interim Summary Report5”
by the Information Economy Subcommittee of the Ministry of Economy, Trade and Industry.

gathering - analysis

analysis results

Utilization ofInformation

Table 3: Active utilization of IoT in agriculture
Case 1 (includes suppositions)
1. Automatic data acquisition with IoT

2. Data integration and analysis
3. Feeding back analysis results into the realworld.

sunshine and other parameters, as well as growth status data.
Harvest forecasts by way of analysis using cloud technologies,
and learning using AI, etc.
Minimize harvesting losses and sales opportunity losses using
forecasts from item 2 above.

4. Value creation and achievement of fullautonomy with AI

 Benefits of IoT utilization:

Acquire big weather data for temperature, precipitation,

Improve harvest forecasts through repeated data gathering and
AI learning.

 Realize stability of supply.
 Minimize harvesting loss, crop disposals, and loss of business opportunities, etc.

gathering - analysis

analysis results

Utilization ofInformation

Case 2 (includes suppositions)
1. Automatic data acquisition with IoT

2. Data integration and analysis
3. Feeding back analysis results into the realworld.

Install sensors and cameras in the field to monitor soil andgrowth status to acquire data automatically.
Discover optimum modes of action (temperature control,
watering, etc.) based on cloud analysis, AI learning, etc.
Implement temperature control, watering, etc. using resultsfrom item 2 above.

4. Value creation and achievement of fullautonomy with AI

Use results from analysis to automatically control andimplement the above tasks.

=Benefits of IoT utilization:  Reduce workloads. (Reduce consumption of energy used for personnel transport. Resolve
labor shortages.) Improve quality and stability of yield through precise temperature control, watering, etc.
 Pass on of skills by integrating knowledge assets.

5"Interim Summary Report" (Apr. 2015) by the Information Economy Subcommittee, Industrial Structure Council, Ministryof Economy, Trade and Industry.
http://www.meti.go.jp/committee/sankoushin/shojo/johokeizai/pdf/report01_02_00.pdf

22

4.2.2

Specific examples

As shown in Table 3, the benefits of IoT utilization in agriculture cannot be fully represented as equivalents
to CO2 emissions reductions. Therefore, while trial evaluations using CO2 conversions were not carriedout for agriculture in this report, we would like to present a number of actual cases below.

[Case 1] Contributing to the environment through the reduction of
harvesting loss and crop disposals based on harvest
forecasts. (JSOL Corporation)

Crops: Lettuces, cabbages, etc.
Implemented regions: Nagano Prefecture, etc.
Key points from an IoT perspective, and Green Benefits:
 Enable advanced overall management of agricultural businesses, including areas such as
production and shipment adjustments, based on harvest forecasts that are based on work dataacquired from production sites and open data (weather, etc.).
 Help reduce harvesting loss by introducing resources during harvesting season in a calculated way
based on harvest forecasts, improve capabilities to respond to supply and demand fluctuations
through partnerships between production regions (complementation), and minimize the amount ofcrops to be disposed of.
 Because harvesting loss and crop disposals result in the wasteful consumption of land, water,
fertilizer, labor and other resources that are used for production, and also cause unnecessary CO2emissions, we will be able to contribute to the environmental cause by improving the precision of
harvest forecasts.

 Weather data Geographic data

 Data on actual harvest results fromproducers

Production

2nd Data: Harvest forecast

 Harvest timing and volume forecast

Demandforecast

Contractmanagement

3rd Data: Agriculturalbusiness decisions

Harvestforecast

4th Data: Supply forecast
Sales(Manufacturing, processing,
distribution, and retail)23



Refine business decision-making based on
harvest forecasts.
Ex.(1) Reduce costs of disposal by optimizing
production and demand.
(2) Improve selling price and ensureprofitability by making sales adjustments
based on market conditions.
(3) Optimize labor costs through HR
management during harvesting seasons.

[Case 2] Improvements to work efficiency and quality through thevisual representation of temperature control know-how.
(Fujitsu Limited)
Crop: Corn (sweet corn)
Implemented region: Yamanashi Prefecture
Key points from an IoT perspective, and Green Benefits: A multi-sensing network equipped with cameras was installed in sweet corn fields. Temperature
control know-how of experienced famers was visually represented to improve the production
skills of new farmers. Farmers were able to remotely monitor the temperature of their fields inreal-time on their smartphones, and this reduced the labor associated with checking temperaturesin fields that were scattered over several locations, and reduced the amount of fuel consumed fortraveling as well as man-hours required for such work.
 Additionally, the number of temperature control failures were reduced to less than half the levels
prior to implementing the solution, and the yield of marketable produce increased by 10-20% overthe previous year (quality improved).

Using ICT to visually represent temperature controls and support new farmers

Agricultural Research Center(Yamanashi prefecture)

JA Nishiyachiyo, In-Season Produce
MarketAnalyzeandevaluate
data

Recordand utilize
data

Carry out ventilation work

Data analysis

System utilization and evaluation

Discover findings

Feedback to worksite

Present findings

Fujitsu
Gather and accumulate sensor data and still images
Visual representation of temperature control (Since the second year
after dashboard implementation)

Provide system

Cloud service

Provide system"Farming by Yamanashi Corporations" program

Websitehttp://www.fujitsu.com/jp/about/environment/society/solutions/sustainability/case-studies/0
3/result/index.html

24

4.3

What we have found in trial evaluations

As we have mentioned in Section "3.2 Items to be included in the evaluation," subjects chosen for thesetrial evaluations as contributions by IoT solutions include subjects that were not included in previous
calculations of CO2 emissions reductions, as well as those that may involve non-CO2 equivalent metrics ofevaluation and expressions. Calculations of the contributions of these subjects were a topic of debate. Suchsubjects include, for example, "reducing food product waste," "resolving labor shortages," and "increases in
the power and energy consumption from data processing." The topics of debate were the ideas of"categorization," "evaluation," and "expression." Another aspect of these evaluations was that there has
been an increase in man-hours spent for quantification due to the expansion of the scope of calculations
that resulted from IoT utilization.
For referential purposes, we present the discussions carried out by the Green CPS/IoT Review Working
Group during fiscal years 2015 and 2016 below.

4.3.1

New subjects for evaluation(1) Reducing food product waste(1) Category
The disposal of food products, or "food product waste," has become a societal issue, and wehave found that the utilization of IoT solutions in logistics and agriculture can be expected tohave an effect on waste reduction. In the area of logistics, evaluations were carried out withrespect to CO2 emissions reductions. That being said, because this subject did not belong to anyof the calculation categories from (1) to (7) in Table 2, it was included in "(8) Other" for thetime being to convert its effects to CO2 emissions reductions.
Meanwhile, some suggested that a new "Benefits" framework should be created to evaluate andexpress non-CO2 subjects in addition to evaluations made for CO2 emissions reductions.
(2) Evaluations
With respect to methods of evaluation for food waste reduction, conversions to CO2 inexamples for logistics were made using publicly available data, such as food products CFP(carbon footprint) and LCA. In the <Reference> section below, we have listed URLs where
utilizable data is available. If data for a specific product is not available, provisional calculations
can be made through inference using similar data. That being said, inference may be difficult insome cases.
<Reference>  Ministry of Economy, Trade and Industry CFP (carbon footprint) program trial
project
Examples of provisional calculations for fiscal 2009 thru 2011
http://www.cms-cfp-japan.jp/info/index.php
 CFP Program Product, Japan Environmental Management Association for
Industry
https://www.cfp-japan.jp/info/index.php
 Ecoleaf, Japan Environmental Management Association for Industry
http://www.ecoleaf-jemai.jp/

25

(3) Expressions
While evaluations in this report express results as equivalents to CO2 emissions reductions,
there may be other means of expression that enable us to communicate, in ways that are easierto understand, the fact that IoT is beneficial in the resolution of many societal issues. Moving
forward, we would like to continue our reviews.
(4) Summary
In this review, our primary aim was to cover the contributions that IoT solutions can make inour societies and can be easily considered within the environmental perspective. These included"food product waste" and other subjects that were relatively more difficult to convert to CO2emissions, as well as those for which no method of conversion could be devised. How can weevaluate and express these environmental contributions in ways that can be readily understoodby the general public? The following three proposals were raised in discussions in our working
group:[Proposal 1][Proposal 2][Proposal 3]

Convert to CO2 emissions and add into the total with other elements. (Devise
methods of conversion)
Do not convert to CO2 emissions. Express in different units in a different
framework than the summed CO2.
If an element can be converted to CO2, convert it to CO2 emissions and add into
the total with other elements. Other elements may be expressed in different units
in a different framework.

Apparently, discussions are currently underway at logistics-associated organizations on the
topic of reducing food product waste. Moving forward, we would like to pay close attention to
discussions being carried out by associated organizations, and continue our reviews.
(2) Resolving labor shortages
We have found that the utilization of IoT can be expected to resolve labor shortage issues in both
agriculture and logistics. While it would be possible to make conversions to CO2 emissions using
traditional evaluation methods based on reductions in per-person energy consumption in theworkplace, we intend to review other methods of evaluation and expression as the resolution of thisissue is of great societal concern.

4.3.2

Factors that cause energy consumption to increase(1) Power and energy consumption at cloud data centers
The calculation method "(6) Electricity and energy consumption (IT and network equipment)"
shown in Table 2 must take into account the rise in energy consumption that results from operations
such as communication of data gathered through IoT and analysis processes carried out at clouddata centers. In the case reviews in this report, the power consumed by servers, storage devices,
network devices, and other devices that make up the data center was calculated and summed using
traditional methods. This process was extremely man-hour intensive.
As a new proposal for future reviews, some voiced that it would be more appropriate to consider
cloud data centers to be a single package, including all of its constituent devices, and define them asa primary unit. We plan to review these questions on an ongoing basis, including whether a defined
scope (boundaries) for the evaluation, or evaluation parameters has been put into place.

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5.

In approaching future reviewsPerspectives centered around the environment: Provisional calculations of total CO2 emissions
reductions
The utilization of IoT has just begun, and a variety of solutions based on new ideas are being created. The
primary subject of our reviews in this report are the contributions made by IoT solutions to society, basedon which we have carried out trial calculations for CO2 emissions reductions for a number of cases inlogistics. While these are still limited to the numerical quantification of results from each case, we intend touse these as our starting point, and conduct provisional calculations of the total amount of CO2 emissions
reduced in specific areas, taking into account the popularization of IoT solutions.


Perspectives that derive from the environmental perspective: Reviews on the evaluation andexpression of "Green Benefits"
We have reviewed several examples of IoT solution utilization in the areas of logistics and agriculture, andhave found that not all environmental contributions were necessarily readily convertible to CO2 emissions.
Additionally, as mentioned at the beginning of this report, the number of topics that are being discussed in
the context of the environment has been growing in recent years, and we can expect to see active utilizationof IoT solutions for a variety of purposes, including enabling consistent crop yields, resolving labor
shortages, and improving comfort levels in homes and offices to name a few. Moving forward, we intend to
gradually expand our environment-related visions to "adaptation," "smart wellness" and other areas toevaluate and express these areas in terms of their Green Benefits. To this end, calculation methods will
have to be improved or created anew as we move forward. In reviewing these approaches, we will be
required to sort out various points in question, such as "calculation category," "evaluation methods," and"methods for evaluating results."
 In closing
In terms of the environment, IoT solutions are expected to make major contributions to CO2 emissions
reductions. But not only that, they should be able to make societal contributions that lead to happiness overa much wider spectrum. We intend to carry out further reviews moving forward so that we will be able to"visually represent" these possibilities through appropriate methods of evaluation and expression, anddisseminate these results throughout society.

27Reference Documents
Calculations for CO2 emissions reductions for the cases included in this report are based on methods
developed by the Green IT Promotion Council (2008-2012). Details of these methods are described in
document (1) below.
The line of thinking behind CO2 emissions reductions calculations in this report is consistent with that in
the publications listed below. Please feel free to reference these documents as they are available as freedownloads in their entirety.
------------ CO2 Emissions Reductions Enabled by IT Reference documents(based on the same ideas as this report) -----------

Green IT Promotion Council (fiscal 2008-2012)(1) "Energy-Saving Contributions for Society at Large from IT Solutions-- Perspective on Evaluations of Contributions from 'Green by IT' -- “Explanatory Booklet”(Published Feb. 2013)
http://home.jeita.or.jp/greenit-pc/activity/reporting/110628/pdf/survey02.pdf(2) Comprehensive Report from the Survey and Analysis Committee of the Green IT Promotion
Council-- Contributions of Green IT for a Low-Carbon World -- (Published Feb. 2013)
http://home.jeita.or.jp/greenit-pc/activity/reporting/110628/pdf/survey01.pdf

Green IT Committee, JEITA(3) Study and Research Report on the Energy-Saving Effects of Utilizing IT-- Energy-Saving Effects of Implementing BEMS in Buildings and Shops -- (Published Mar.
2015)
http://home.jeita.or.jp/greenit-pc/bems/pdf/bems2.pdf(4) Report on the Framework for Quantifying IT Solutions' Contributions in CO2 Emissions
Suppression-- Umbrella Method for Calculation and Aggregation -- (Published Mar. 2017)
http://home.jeita.or.jp/greenit-pc/contribution/pdf/it-framework.pdf

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