Mitsubishi Heavy Industries Technical Review Vol. 55 No. 4 (December 2018)
1Improving Ovarall Performance of Factories– Launch of ENERGY CLOUD FactoryTM –RYUJI IKEDA*1HISASHI NISHIKI*2

HIKARU SAWADA*3

To maintain global competitiveness, it is necessary for companies to not only provide
appealing products and services, but also differentiate them in consideration of quality, cost, anddelivery (QCD) of energy usage such as the reduction of CO2 emissions in the process, in addition
to QCD of manufacturing.
The ENERGY CLOUDTM Service is a comprehensive energy solution service provided byMitsubishi Heavy Industries, Ltd. (MHI), and ENERGY CLOUD FactoryTM is its factory-focusedpackage. We use AI&IoT technologies to integrate MHI's expertise in manufacturing and energy
usage with ENERGY CLOUD FactoryTM and have been promoting overall optimization including
individual factory optimization and global corporate management for domestic and overseas baseswith no limitations from regionalism.
This report introduces the ENERGY CLOUDTM Score, which is a unique KPI (keyperformance indicator) developed for this purpose, and its related display system.

|1. Introduction
Our group manufactures products ranging from the production equipment that forms thebackbone of production processes to the power generation equipment that is necessary forproduction. Through such activities as a manufacturing company, our group possesses various
types of manufacturing and energy usage expertise.
In addition, as an effort toward the optimization of manufacturing, each manufacturing site isproceeding with IoT promotion activities. Specifically, the utilization of a scheduler andvisualization at a factory level are realized and groups of data scientists that analyze acquired data
are organized.
On the other hand, to improve corporate value, generally, it is necessary to objectively grasp
the entity’s own performance with a KPI. Within our group, we are monitoring manufacturing sites
using various KPIs according to the product characteristics of the factory. There is a strong need toutilize KPIs that are objective and comparable between factories to improve factory performance.
For this reason, it is generally assumed that many companies have similar needs.
Therefore, by combining various types of manufacturing expertise, which is a strength of ourgroup, with proprietary AI&IoT technologies cultivated through energy solution services, we have
independently defined the ENERGY CLOUDTM Score, which is a KPI that enables thecomprehensive evaluation of a factory's performance from both aspects of manufacturing andenergy. The ENERGY CLOUDTM Score has the following three features.
(1) A KPI that considers not only manufacturing, but also energy use performance.
(2) An objective and global KPI that leads to the extraction and improvement of problems atthe manufacturing site and enables performance comparisons between factories.
(3) A KPI that can lead to far-sighted business operations through not only the visualization of*1

EPI Department, ICT Solution Headquarters*2

Program Execution Manager, Power & Energy Solution Business Planning Department, Power & Energy Solution Business*3

Management & Administration Department, Mitsubishi Heavy Industries Aero Engines, Ltd.

Division, Power Systems

Mitsubishi Heavy Industries Technical Review Vol. 55 No. 4 (December 2018)2

the current situation, but also prediction.

|2. Development of concept and display system of ENERGY
CLOUDTM Score
In developing the concept and the display system of the ENERGY CLOUDTM Score, weproceeded with the following steps: (1) setting of the KPI concept and prediction of the future, and(2) systematization. The details are as follows.

2.1

KPI concept and future prediction

Traditionally, both the manufacturing site and utility management department individually
worked on the analysis and optimization of manufacturing and energy usage.
In response to that task, we independently defined the ENERGY CLOUDTM Score, which is
a comprehensive evaluation KPI that combines manufacturing and energy, and facilitated theevaluation of the comprehensive performance of the factory from both aspects of manufacturingand energy. Figure 1 shows the concept of the ENERGY CLOUDTM Score.

Figure 1Concept of ENERGY CLOUDTM Score, comprehensive factory evaluation KPIThis ENERGY CLOUDTM Score can be broken down to the KPIs of manufacturing (OEE)
and energy (specific energy consumption), which are the component elements, and can lead to theidentification of problematic KPIs, ex-post evaluation, and the solving of problems.
The selection of the KPIs of manufacturing and energy is as follows.
2.1.1 KPI of manufacturing
Overall Equipment Efficiency (OEE) was selected for the following reasons.
- OEE is defined as "operating ratio" × "performance" × "quality," and QCD is includedin the component elements, facilitating the extraction and improvement of problems at
the manufacturing site.
- OEE is an objective KPI prescribed in ISO 22400(1), which is an international standard
KPI in the manufacturing industry, and is widely applied globally.
2.1.2 KPI of energy
The specific energy consumption was selected for the following reasons.
- The specific energy consumption is the total amount of energy consumption required toproduce a unit amount of product, represents the energy efficiency, and facilitates theextraction and improvement of problems in the utility management department.
- The specific energy consumption is an objective KPI prescribed by the Act on theRational Use of Energy (Japan’s energy conservation law).
2.1.3 Future prediction of KPIRegarding tasks that were in the past handled when they occurred at the manufacturing siteor thereafter, the application of prediction technology using AI can lead to the advancement offactory management using preliminary evaluation such as preventive maintenance andoperation optimization. As shown in the graph on the right of Figure 1, the future prediction
value of the aforementioned KPI was calculated using our unique AI technology which
combines our company's manufacturing and energy usage expertise.

2.2

Outline of display system

For the acquisition of the data to calculate the ENERGY CLOUDTM Score, MHPS Control
Systems’ Netmation eFinderTM, which is an easy-to-install IoT tool that can be incorporated

Mitsubishi Heavy Industries Technical Review Vol. 55 No. 4 (December 2018)3

regardless of the equipment type and is ideal for the introduction of AI, was selected.
By utilizing the acquired facility operation data, a system was constructed that displays theactual values and future prediction values of the ENERGY CLOUDTM Score consisting ofproductivity and energy consumption, realizing ENERGY CLOUD FactoryTM (Figure 2).

Figure 2

System realizing ENERGY CLOUD FactoryTM

Among the component elements of OEE, which is a KPI of manufacturing, “the operatingratio” is calculated by dividing the operating time by the load time, and “the performance” iscalculated by dividing the net operating time by the operating time.
Figure 3 illustrates how to calculate the load time, the operating time, and the net operatingtime of the facility from the facility operation data of the Netmation eFinderTM. The main power
supply current value and the processing signal are measured from the facility as facility operation
data by the Netmation eFinderTM. For the two signal values, predetermined threshold values fordetermining the load time, the operating time, and the net operating time are set. The load time, theoperation time, and the net operating time can be calculated as the time for which the signal
exceeds the threshold value per unit time.

Figure 3 Automatic calculation of load time, operating time, and net
operating time

Figure 4 shows the screen of the system displaying the performance of the entire target
factory. The screen showing the entire factory displays trend graphs of the ENERGY CLOUDTM
Score and its component elements, which are the specific energy consumption and the actual andprediction values of OEE. Figure 5 shows the screen of the system displaying the status of eachfacility. The screen showing the status of each facility displays trend graphs of the specific energy
consumption, the actual and prediction values of OEE of facilities composing the factory and theactual values of the component elements.
By referring to these screens at the manufacturing site, an in-depth confirmation of which
component element affected a change in the value of the ENERGY CLOUDTM Score can beascertained. Therefore, it is easy to analyze everything from the fluctuation of the entire factory tothe confirmation of influencing factors.
In this way, we established the system of ENERGY CLOUD FactoryTM, which performs andMitsubishi Heavy Industries Technical Review Vol. 55 No. 4 (December 2018)4displays the calculation and future prediction of the factory's KPI to indicate the ENERGY
CLOUDTM Score, a KPI that visualizes comprehensive performance objectively from both aspectsof manufacturing and energy.

Figure 4ENERGY CLOUD FactoryTM display system (entire factory)

Figure 5

ENERGY CLOUD FactoryTM display system (each facility)|3. Verification of ENERGY CLOUDTM Score
To evaluate the effectiveness of the ENERGY CLOUDTM Score, verification was performedat Mitsubishi Heavy Industries Aero Engines, Ltd. (MHIAEL), which manufactures and assembles
aircraft engines.
MHIAEL has been working on acquiring manufacturing data utilizing IoT, and has abundant
manufacturing data in its possession. Utilizing this data and opinions from actual manufacturing
sites, we worked on the improvement of productivity and energy cost reduction using theENERGY CLOUDTM Score.
The verification target was the machining process. This process consists of multiple
machining facilities, and after the completion of the process, a machining quality judgment test isimplemented.
We installed Netmation eFinderTM on the main power supply, spindle power supply, etc., ofMitsubishi Heavy Industries Technical Review Vol. 55 No. 4 (December 2018)5

these machining facilities, acquired facility operation data and verified how to utilize the actual andfuture prediction values of the ENERGY CLOUDTM Score.
(1) Utilization of actual values
By comparing the change of OEE constituting the ENERGY CLOUDTM Score with theoperation data of the facility, the standby state of the facility in which the power supply of theequipment is turned on but no processing is performed could be visualized. By shortening these
unnecessary standby times, it was found that the shortening of the lead time and the reductionof energy cost could be expected.
(2) Utilization of future prediction values
Among the KPIs of operating ratio, performance and quality, which are component
elements of the ENERGY CLOUDTM Score, the prediction of the quality KPI was focused on
because it was highly demanded by the manufacturing site.
The quality prediction model can be obtained by analyzing facility operation data andinspection result data. The quality prediction model can predict the degree to which theprocessing target is determined to be good or bad after processing. By incorporating this quality
prediction model into the system, the degree of good or bad judgment can be derived as aquality KPI. The system can list the predicted value of the quality KPI and facilities with astrong correlation therewith. Engineers can prioritize efforts to solve problems such as theimprovement of the quality of the listed facilities, which leads to the improvement ofproductivity.
As mentioned above, we were able to determine that the measured values and future
prediction values of the ENERGY CLOUDTM Score can contribute to energy cost reduction and theimprovement of productivity.

|4. Conclusion
We defined the ENERGY CLOUDTM Score, which combines manufacturing and energy
using unique AI&IoT technologies, developed the display system as "ENERGY CLOUD
FactoryTM," and verified them at a factory of our group company.
We will continue to introduce "ENERGY CLOUD FactoryTM" to several factories within ourgroup and evaluate its effectiveness by applying the ENERGY CLOUDTM Score to various
manufacturing sites. In parallel, we will promote the incorporation of new KPIs that will beeffective, as well as the development of global optimum evaluation across multiple plants as newsolution services.
In addition, we are preparing to provide our customers' factories with the knowledge gained
through the efforts at our group factories, aiming to contribute to the overall optimization ofmanufacturing and energy at as many factories as possible.
ENERGY CLOUDTM, ENERGY CLOUD FactoryTM, and all related marks and logos are registered trademarks of
Mitsubishi Heavy Industries, Ltd. in Japan and other countries.
Netmation eFinderTM and related marks and logos are registered trademarks of Mitsubishi Hitachi Power
Systems, Ltd. in Japan.

References(1)

ISO- 22400 :2014