SMKL (Smart Manufacturing Kaizen Level) for
Smart Manufacturing Transformation

Nicole Shonan Otoki

Mitsubishi ElectricCorporation

Yasuo Onodera

Mitsubishi ElectricCorporation

1. Introduction

The trend toward digital transformation has been gaining
momentum in recent years, and smart factories implemented by
the Industrial Internet of Things (IIoT) are attracting attention for
their importance to digital transformation in the manufacturingindustry.
Using various kinds of information in digital form
enables smart factories to make the interconnections among
manufacturing processes smarter and optimize the value chain.
Although many companies are aware of the enormous value thatsmart factories can create, surprisingly few are making steadyprogress toward smarter factories.
However, there are many challenges in the path toward
smart factories, including advanced automation, softwarebased management, and the establishment of platform-based
ecosystems. It is thus necessary to deploy IIoT technologies toappropriate places in manufacturing and coordinate them toensure a reasonable return on investment (ROI) while achieving
continuous improvement.
Given that situation, companies are seeking methods forevaluating current ongoing IIoT initiatives and methods forcontinuous optimization based on Plan Do Check Action(PDCA) and return on investment (ROI). We also believe that it
is essential for companies to understand the maturity level of IIoT.
To address those issues, we position maturity assessment as
an important approach for companies in the promotion of IIoTand define a path to establishing smart factories using maturity
assessment.

2. M
 aximal use of digitalization in smart
factories

IIoT will have application in all aspects of the manufacturing
world opened up by digital technology. The move towards smart
factories has begun; big data collected from shop floors is being
used to improve the productivity and quality of facilities and work,and digital technology is being used to link supply and engineering
chains.
To cope with such changes, the e-F@ctory (Figure 1) provides
an integrated factory automation (FA) solution that supports
total cost reduction across the supply chain and engineering
chain and improving its corporate value by handling flexible
manufacturing through coordination of workers, machines, andIT toward the objectives of implementing and optimizing smart

Mitsushiro Fujishima

Mitsubishi ElectricCorporationfactories, beginning with the shop floor. The e-F@ctory provides
a product lineup for linking the shop floor and the IT system toenable seamless networking from the FA side to the IT side andpromote optimization based on the level of maturity of the shopfloor and analysis of big data.
■ Figure 1: The e-F@ctory Integrated FA solution

3. Smart factory that enables continuousoptimization

The smart factories based on the e-F@ctory solution that wehave seen so far are multidimensional, comprising a combinationof many IIoT technologies. For example, bringing together
multiple innovative technologies, such as big data analysis,
artificial intelligence, and robotics, provides the perfect means ofoptimizing the value chain.
While a smart factory is a very complex endeavor, it is aprime motivator of business success by creating new value. Here,companies need to answer “what,” “when,” “how cost effectively,”and “to what level” questions as they move forward in their smart
factory journey. Companies also need to visualize the maturity
level of IIoT adoption and use that as a basis for determining ROI.
SMKL (Smart Manufacturing Kaizen Level) evaluates thelevel of maturity of IIoT using Key Performance Indicators (KPI)
specified by the ISO 22400 international standard and identifies
problems and plans for optimization for individual companies. It
also indicates the ROI of improvement proposals and goes intomeans of promoting step-by-step progress toward smart factories

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to achieve continuous optimization.

■ Figure 3: Management levels

3.1 Definition of SMKLSMKL (Smart Manufacturing Kaizen Level) is a simple
assessment of IIoT maturity based on a matrix of five levels ofmaturity and four levels of management (Figure 2). The maturitylevels are data collection, visualization, analysis, optimization,and prediction (Table 1). The management levels for discrete
manufacturing are defined as installation and workers, workshop,factory, and supply chain (Figure 3).
■ Figure 2: Definition of SMKL

3.2 Application of SMKLThe steps of using SMKL are illustrated in Figure 4. In thefirst step, the KPIs are selected according to the managementtargets which are set by the SMKL users (managers, production
managers, workers, etc.). Then, the KPIs are used to determine
the current SMKL (“current point” in Figure 4) and thedifferences between the current values and the target values
in the SMKL matrix are denoted. Possible implementation
procedures for achieving the targets are also confirmed. Next,
ROI is used to determine the feasibility of each implementation,
and appropriate targets and implementation procedures areset (Figure 4). Afterwards, the details of the implementation
procedures are checked by the stakeholders (such as factory■ Table 1: Maturity levels

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■ Figure 4: SMKL application

promising faster and more effective optimization compared toconventional approaches.

4. Case study

managers, production managers, etc.) and a consensus decision isreached. In the final step, the SMKL target levels (“target points”
in Figure 4) are achieved by continuous optimization based on
SMKL assessment.
SMKL can be used to promote adoption of IIoT byindividual companies and assess the direction of adoption toenable continuous investment decisions. Doing so will enable
implementation of the right IIoT products at the right time,

The effort towards carbon neutrality in manufacturing has
become increasingly important in recent years. The 2021 White
Paper on Manufacturing raises an urgent need for a transition
to green manufacturing by introducing digital technology. Asa means of promoting energy conservation by improvements
in operations, achieving energy efficiency and efficiency in theoptimization cycle through energy management and use of IIoT,
and integrated control technologies for production management
and energy management have been attracting attention.
However, a problem for the executives of many manufacturing
companies is the difficulty in making investment decisions forlack of knowledge of their current level of energy management
and what solutions are appropriate for improvement. Here, we
provide an example of applying e-F@ctory for energy managementand describe how optimization using SMKL achieves energy
conservation.
As a means of promoting energy conservation by operational
optimization using IIoT, we are taking a step forward in theevolution from conventional optimization of individual facilities
in a factory toward overall optimization of the entire factoryas an integrated system of linked facilities. Accordingly, the■ Figure 5: SMKL example application: Assessment of energy conservation(specific energy consumption)

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assessment example of using SMKL shown in Figure 5 targets
three management levels: Installation & Worker, Workshop,and Factory. Each management level is assessed for all maturity
levels from Collection (Level a) to Prediction (Level e). Aftercompleting the Level e assessment for Installation & Worker,
the visualization and analysis steps for a Workshop and Factory
are easily accomplished. The assessment criteria and examples ofthe related IIoT technology for each SMKL in this case study arelisted in Table 2.
Specific energy intensity is a widely used KPI for energy
conservation. The machine availability rate is also an important
assessment point. Differences in cost effectiveness associatedwith choice of KPI related to specific energy are presented inFigure 6. Factory managers can begin with KPI that have high■ Figure 6: SMKL example application: KPI used inassessment of specific energy consumption

cost effectiveness with the objective of achieving a future energyefficient smart factory through adoption of IIoT and stepwise
improvement of SMKL. Machine designers can refer to theselected KPI to determine the current SMKL. The upgrading
criteria indicated by SMKL can also be considered in decisions
on improving existing facilities and introducing new facilities.
Production managers can use the defined managed items andmanagement target levels in the execution of PDCA (Plan-DoCheck-Act) cycles to achieve their goals.

5. Conclusion

IIoT initiatives that are appropriately tailored to the business of
the individual company are essential to achieving a smart factory.
Data-driven IIoT requires collection of various types of data fromthe shop floor, and digital transformation of the entire supply
chain and engineering chain requires a high level of investment
and time before optimization is actually achieved. Nevertheless,the SMKL described in this paper can be used to guide thepromotion of IIoT in companies, so acceleration in achieving
smart factories can be expected.
To further invigorate the future IIoT market, we will also
work on openness and international standardization at the IAF(Industrial Automation Forum) and promote development ofproducts and solutions that can be mapped to the SMKL matrix
with the objective of contributing to the digital transformation of
the customer’s organization and global development of the IIoTmarket.

■ Table 2: SMKL example application: Criteria used in assessment of specific energy consumption

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