Received: 18 February 2020 Revised: 17 April 2020 Accepted: 29 May 2020 The Journal of Engineering
DOI: 10.1049/tje2.12008
ORIGINAL RESEARCH PAPER
Smart electric motor: Evaluation of business potential for
digitalisation in the large electric motor industry
Timothy Joe Costello1Kai Strunz2Joachim Müller-Kirchenbauer3
1Digital Industries, Siemens, Berlin, Germany
2Faculty of Electrical Engineering and Computer
Science, Technische Universität Berlin, Berlin,
Germany
3Faculty of Business and Management, Technische
Universität Berlin, Berlin, Germany
Correspondence
Timothy Joe Costello, Digital Industries, Siemens,
Berlin, Germany.
Email: [email protected]
Funding information
Open Access Publication Fund of TU Berlin
Abstract
Digitalisation is seen to have contributed to the demise of 50% of the Fortune 500 compa-
nies since 2000. The large electric motor segment is not immune to this changing business
landscape and must evolve to survive. This sector is experiencing declining revenues as
traditional hardware-sales-focused business models have proved inadequate to address a
reduction in profitability. New business models that exploit new digital value streams are
to leverage the value from emerging technologies of the Industrial Internet of Things, Big
Data and the associated emerging predictive analytics. These can be optimised via Cloud
Computing and delivered utilising Platform as a Service software solutions to reduce cost
and support scalability. The missing link in the justification chain for the installation of dig-
italisation packages on large electric motors is a model to determine if and which package
sections return the most customer value and, therefore, sales opportunities for the supplier.
In this context, this paper introduces the concept of the smart electric motor. The focus
is on the oil and gas industry, where digitalisation is already deeply engrained in practice
and shows how risk-based decisions can deliver a profitable project through new business
models and digitalised solutions to reinvigorate this traditional industry.
1 INTRODUCTION
In 1963, Leon C. Megginson summarised the sentiments of
Charles Darwin’s Origin of Species with “It is not the strongest
of the species that survives, nor the most intelligent that sur-
vives. It is the one that is the most adaptable to change”. He
was of course referring to the natural world; however, this sen-
timent can be applied to modern industry and the inevitable
drive towards digitalisation. It has been said that digitalisation
is the main reason just over half of the companies on the For-
tune 500 have disappeared since 2000 [1], and it is estimated that
this trend will continue into the future. For such companies who
produce large electric motors (LEMs), this is a risk, which must
be addressed to remain competitive.
Although the digital transformation of the LEM industry is
still in its infancy, the oil and gas (O&G) digitalisation expendi-
ture is already significant; in 2015, the sector spent $7 Billion on
Internet of Things (IoT), $6 Billion on cloud services and $ 3.5
Billion on Big Data, which are all expected to increase signifi-
cantly by 2020, as shown in Figure 1.
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© 2021 The Authors. The Journal of Engineering published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology
The O&G industry faced significant challenges over the past
years since the oil price (West Texas Intermediate) dropped
from over $100US/bbl in August 2014 to below $30US/bbl
by January 2016 when adjusted for inflation [3]. This has
resulted in the O&G companies reducing expenditure and
placing more pressure on the justification of project funding
[4].
A linking element in the digitalisation of LEM business mod-
els is presented in this paper: the net present value (NPV) of
a digitalisation project. This paper defines LEMs as those over
375 kW and will address the question: Can digitalisation increase
value in the LEM segment and reinvigorate this old industry?
To address this question in more detail, the variables that have
the most significant influence on a digitalisation project’s value
have been identified and evaluated, and the variables have been
assigned to either the customer or supplier as they are respon-
sible for obtaining this data. The key variables have been evalu-
ated as to how easy or difficult this data is to obtain as this is the
final step in realising the potential of a digitalisation project for
LEMs.
J. Eng. 2021;2021:25–36. wileyonlinelibrary.com/iet-joe 25
26 COSTELLO ET AL.
$7B
24% CAGR
$22B
$3.5B
31% CAGR
$6B
CAGR of 28%
FIGURE 1 O&G IoT, Big Data, and Cloud Services expenditure [2]
The missing link in the justification chain for the installation
of digitalisation packages on LEMs is a model to determine if
and indeed what sections of a package return the most value
to the customer and therefore sales opportunities for the sup-
plier. A customer value model that addresses the missing piece
of a calculable NPV for a digitalisation package for LEMs was
developed to provide the end customer with a justification for
the digital solution investment.
The business opportunity is driven by both the value to the
customer and the value to the supplier as the digitalisation of
LEMs has very different value streams, depending on which
side of the partnership you are on. It is critical to understand
both sides of this equation in order to fully develop and exploit
the value in the digitalisation opportunity. Once the different
value streams have been identified, the key value of the asso-
ciated customer process is fed into the model for a real-world
case study. This uses existing technology and plant and the inter-
action between a supplier’s hardware and software costs, and a
customer’s process value stream. In the worked example, the liq-
uefied natural gas (LNG) throughput is used. Sensitivity analysis
of key quantities is finally performed, analysed and conclusions
drawn.
2RESEARCH STATUS
2.1 LEM suppliers and digitalisation
Due to the extremely rapid rate of change within the digitalisa-
tion business world, this paper focuses on both recent business
white papers and industry publications as well as academic liter-
ature on the subject. A key piece of research has been produced
by the World Economic forum entitled “Industrial Internet of
Things: Unleashing the Potential of Connected Products and
Services” [5]. The report predicts that the IoT revolution will
drastically alter the manufacturing, energy, agriculture, trans-
port, and other industrial sectors over the next 10 years. These
are industries that together account for two-thirds of the global
gross domestic product. It finds the industrial Internet to be
transformative and predicts unprecedented opportunities with
the Industrial Internet of Things (IIoT) along with new risks to
business and society [5].
Both Siemens and General Electric misjudged the growth in
both the large electric drive market and gas turbine market in
their power generation units. As such, both companies have had
to adjust their associated business units. Siemens has proposed
in 2017 a 6900-person reduction [6] and GE a 12,000-person
reduction [7]. The Siemens MindSphere paper has a focus on
the IoT and the connection of the real world with the virtual
world of data. They propose an underlying structure of Software
and Services, overlaying the MindSphere cloud IIoT operating
system to enhance their separate business units, including power
generation and the O&G industry [8].
The ABB whitepaper also provides an O&G digitalisation
focus. Their method is to allow O&G companies to get a highly
granular view of their assets, which, when viewed in conjunction
with data from more traditional business systems, can generate
quicker and better insights to drive competitive advantage [9].
Again, the focus is that for companies to benefit significantly
from the potential offered, they will need to embrace digitalisa-
tion on a bigger, much more holistic scale encompassing end-
to-end processes throughout plants across the supply chain—
not just in isolated pockets of change. This is a common theme
among companies; however, it should also be highlighted that
there is a significant financial benefit to the companies selling
the digitalisation solutions. It will be incumbent upon them to
deliver business solutions with verifiable results through con-
crete financial reporting, such as an improved project NPV,
which is currently not in place and the subject of this paper.
Schneider Electric breaks the transformation of the O&G
industry business models via IIoT concepts down into three key
factors: decreasing cost of connected sensors, increased connec-
tivity, and new software analytics utilising real-time data [2]. The
paper claims that the backbone of the transformative IIoT trend
is the linkage of connectivity, cloud and analytics technologies
to simplify process automation [2]. The paper also highlights
that there are “opportunities to link multiple platforms oper-
ated remotely from a single onshore centre or to deploy remote
monitoring for onshore and offshore operations can dramati-
cally reduce the need for physical on-site inspections”. While
this statement is correct, this is not new to the industry and cer-
tainly not unique to Schneider Electric. The Australian O&G
company Santos, amongst many others, implemented such a
program for their onshore gas fields over a decade ago, more
recently also including full video conference facilities at their
remote partially manned field locations, which required a fibre-
optic cable to be laid with the trunkline to enable an Internet
connection in an otherwise off-grid location.
GE’s Playbook white paper boils down digital industrial
transformation into five key pillar initiatives: capabilities and
operating model, platform, partner ecosystem, digital talent and
culture, and business model innovation [10]. This paper has its
main focus on capabilities and business model innovation as it
applies to the LEM segment specifically. The paper states that
the current age of the digital industry is marked by ever more
powerful software and falling hardware costs. That is, today’s
predictive algorithms and machine learning capabilities enable
use cases that were not possible even five years ago. This is a
common theme throughout all four companies’ white papers.
A key piece of market research performed by GE in October
2017 found that the importance of the digital transformation
COSTELLO ET AL.27
TABLE 1 To what degree do you consider the following to be a barrier to
Digital Industrial Transformation? [13]
Power/Energy Utilities
Base size n =50 n =50
Investment costs 40% 36%
System security concerns 40% 40%
Data privacy concerns 36% 34%
Operating costs 26% 26%
Legacy systems/technology 42% 28%
Lack of clarity on return on investment 26% 36%
Lack of qualified workforce/skillsets 40% 16%
was ranked highly at 78.3% for the outlook, against a company
readiness of 55.2% and a workforce readiness of 63.4%. This
shows that the companies are aware at some level that there is
significant work to be done in order to reach their digitalisation
goals [11]. All four companies have their own next generation
software platforms—Siemens MindSphere, GE Predix, ABB
Ability, and Schneider Electric has EcoStruxure—to facilitate
the digitalisation of LEMs [12]. The importance of collaborative
relationships in business markets are common themes for cus-
tomers and suppliers alike. Customers need to decide whether
to invest in a new supplier relationship, to maintain and develop
a valued relationship, or to divest from a low-value relationship
[13]. While all four companies have an extensive digitalisation
road map, with comparable drivers and motivations, what none
have provided is a concrete path to deliver a LEM digital strat-
egy to identify and deliver the different value streams made pos-
sible by the digital infrastructure for the O&G industry.
2.2 LEM customers and digitalisation
The key LEM sectors of power/energy and utilities believe that
between 54% and 58% of both the individual companies and
the overall industries are primarily responsible for ensuring that
the workforce is ready for the demands of IIoT [11]. This aligns
with the proactive approach seen in the leading companies’
white papers [2, 8–10] as well as the World Economic Forums
industry overview [5]. Collaboration between the industry, their
partners, and clients can be regarded as the backbone of valu-
able business decisions as input from both the customer and the
supplier is required to deliver a successful project.
A successful digitalisation program is bound to IT function-
ality; however, the level of integration will also depend on the
roles those the company performs internally and those that are
contracted out. For example, if the LEM digitalisation analytics
are all performed externally via cloud platforms, the integration
of internal IT personnel is potentially less important than if they
are performing the same analytics in-house.
Common to both power/energy and utilities are investment
costs of 40% and 36%, which can be seen in Table 1. The invest-
ment cost barrier being one of the highest across the industries
shows the need for a value proposition that addresses the costs
of a system as well as the value, and this will be addressed in
this paper. System security concerns and data security are also
ranked highly as barriers. This is a common theme throughout
the companies’ white papers as well as the World Economic
Forum document. Contrary, however, to this concern, Arm-
brust et al. claim that “…there are no fundamental obstacles
to making a cloud computing environment as secure as the vast
majority of inhouse IT environments” [14].
Due to the extremely fast-moving nature of digitalisation,
expert interviews were required to fill gaps in the official pub-
lications. Six experts from the LEM industry were interviewed
to fill in gaps identified by the literature review. Open questions
were used to allow adequate breadth of discussion and give the
experts the flexibility to get to the root of the question from
their professional positions.
3METHODOLOGY
Both a literature review and expert interviews have been con-
ducted to obtain the necessary information to perform analy-
sis on both the customer and supplier value models proposed
for digitalisation of LEMs. Industry analysis has been done to
provide further qualitative insights into value streams, which are
outside the scope of the case study. The case study quantita-
tive and qualitative analysis as well as industry analysis are finally
combined in the results.
3.1 New value models: Why invest?
The missing piece for the justification for the installation of
digitalisation packages on LEMs is a model to determine what
sections of a package return the most value to the customer
and, therefore, sales opportunities for the supplier. With this
justification, a model has been created with the inputs labour
charge-out rates, OPEX and CAPEX costs as well as down-
time improvement likelihoods from the supplier. The customer
inputs are the number of installed units, process value, and his-
torical maintenance data. It is tailored specifically for the O&G
industry, where the concept of deferred production rather than
loss is at the core of the business. The model output is divided
into two key sections: Decision Tree for Digitalisation Selection
and Sensitivity Analysis.
The Decision Tree has been designed to accept the input vari-
ables from both the supplier and the customer to determine the
most efficient digitalisation installation. Depending on the input
variables, the output will determine which units justify the instal-
lation of a digitalisation package and ultimately, the NPV of the
project. This will, therefore, provide the customer with concrete
financial justification and a starting point for project kick-off.
This will change the sales pitch from “we can provide potential
savings” to “from the values you have provided, we will save
you this amount of money over the next five years”.
The uncertainty in many of the input variables means that
it is critical to evaluate the relative importance between them.
This provides value to both the customer and the provider as
the dependent variables need to come from both parties and,
28 COSTELLO ET AL.
1
FIGURE 2 Customer value proposal (Own figure.)
hence, will have a contributing factor on the final project jus-
tification. The analysis will, therefore, highlight which variables
are more important and, therefore, warrant further investiga-
tion and potentially upfront resources to reduce the uncertainty
in the final project NPV.
The supplier value model has been reviewed as the second
half of the overall value model. The supplier has different value
drivers than the customer, and the interactions of these two
parties are required to deliver a successful project. Some value
streams are clear for the supplier: payments for hardware, soft-
ware, and services. The other value streams such as those involv-
ing big data and digital twins are more complicated and are
addressed separately.
3.2 Industry analysis and case study
Industry analysis for an example O&G facility was conducted
and broken down into both customer and supplier project jus-
tification. This breakdown is required as for a digitalisation
project in the O&G industry to proceed on LEMs, there must
be a viable value case for both sides of the sales agreement.
The customer project justification is broken down into the three
key subsections: preventative maintenance, improved forecast-
ing, and other digital value. The supplier project justification is
segmented into hardware, software, and leveraging value from
data.
To prove the value behind a digitalisation business case, a case
study was conducted on the Snøhvit LNG plant in Norway, a
European example of the interaction between a LEM fleet and
a process with significant value. The case study looks at how the
LEMs interact with the process they drive, the value intrinsic
to that process, and deferred production as a key value driver.
This addresses the case quantitatively with a calculable value.
The process and digitalisation improvement values are then fed
into the value stream calculation. The tool chosen is Excel as
it has enough flexibility for the calculations to be re-run for
a different customer and can perform sensitivity analysis. The
quantitative analysis will focus on the customer’s process value
streams in combination with the supplier’s costs and digitalisa-
tion deliverables and a concrete calculation of the projects NPV.
The approach will include sensitivity analysis on critical variables
due to the uncertainty and variability of company data as well as
business sensitive data, which cannot be released.
4MAIN SECTION
The success of LEM digitalisation sales is based on two views:
the customer and the supplier. Both parties must be satis-
fied with their value propositions for a successful digitalisation
project to be delivered.
4.1 Customer value case
The customer value case is broken down into the key subsec-
tions, as shown in Figure 2: preventative maintenance, improved
forecasting, and other digital data value.
To make a significant investment in the digitalisation of
LEMs, the customer will require proof of the value that the
smart LEM will add to their business. This will include items
such as increased reliability and decreased downtime through
preventative maintenance, lower gas market risk, and reduced
inventory through increased forecast accuracy. The key to this
value proposition is the role in which the LEM plays in the over-
all drive train and ultimately the process for which it delivers
power.
The value in smart motors is not limited purely to the value
of the motor itself. In fact, a focus only on the value of the LEM
would typically not be justified as a stand-alone project. As dis-
cussed in the case study in Section 4.3.2,a€2 million motor can
be critical equipment powering a €2.4 million per day process.
The value of the electric motor alone is here not a significant
proportion in the O&G value stream. The process that is driven
by the LEM, however, can be critical to the financial health of
the company.
In the case of the O&G industry, planned downtime via pre-
ventative maintenance is a key piece of the value-add position
for a LEM digitalisation project, where a potential problem
can be identified and the organisation given time to thoroughly
plan for the maintenance activity. This typically results in a
COSTELLO ET AL.29
2
FIGURE 3 Supplier value proposal (Own figure.)
reduction of total time the equipment is out of service as the
correct personnel, equipment, tools, and process conditions can
be pre-prepared. In the case of remote areas such as remote
Central Australia or offshore platforms, this can be a significant
saving. This is confirmed in the case study, which shows the
considerable value realised by reducing the downtime duration
of an event just by a few days over many years. This reduction in
reliability and availability uncertainty allows for improved fore-
casting of the process. Further to this, less maintenance person-
nel are required on permanent staff if there is a lower likelihood
of unplanned maintenance, where contract employees can be
brought in for planned maintenance outages. Improved fore-
casting can also significantly reduce inventory requirements.
As per many primary energy sales, an LNG provider may
have a contract with a specified energy value or tonnage to
be delivered by a pre-defined date. While all contracts are dif-
ferent, it is common practice that a penalty will be built into
the contract for delayed delivery. The likelihood of this penalty
can be reduced by increased forecasting capacity with a digi-
tal condition-based monitoring system. Probabilistic forecast-
ing from digitalised analytical techniques can result in increased
confidence in results, fewer data exceptions, more time for
strategic planning, and the ability to analyse multiple “what–if”
scenarios [15].
The final value in the digital data is much harder to quantify,
but it is conceivable that it will eclipse the direct value on the
production stream. This value stream includes future uses of the
data which may not be clear. This could include improved pro-
cess facility designs, augmented inventory systems or any num-
ber of future data analytics development. The process of gath-
ering data has the potential to help identify these future data
streams as the smart motor industry matures.
4.2 Supplier value case
The supplier value case is broken into hardware and software
sales, as well as leveraging data value, as shown in Figure 3.
Traditionally, the large drive sector has focused on hardware
sales as their primary income stream. This business model has
remained relatively unchanged over the past century with the
sale of hardware being the basis for the profit margin. Secondary
to this, maintenance and service contracts have played a sup-
porting role.
Hardware sales of large drives have been decreasing, for
example the departments of “Process Industries and Drives”
at Siemens saw orders drop from 9.1 to 8.9 Billion Euro [16]:
Oil and Gas” at GE which includes synchronous and induction
electric motors saw equipment orders fall from US$6.6 Billion
to US$3.7 Billion [17], and Discrete Automation & Motion at
ABB a fall in orders from $9.2 Billion to $8.7 Billion [18]. Each
of these companies mentioned declining sales of electric motors
and exposure to the lower oil price being significant contribu-
tors to this decrease. Digitalisation provides an opportunity to
reinvigorate the old industry of LEMs by leveraging new value
streams outside of the traditional hardware-based model.
Major manufactures have the capacity to deliver scalable soft-
ware solutions for customers with almost flat marginal costs.
The scalability is an advantage for customers who otherwise
would have to set up the solution individually. This then opens
the opportunity to provide recurring surveillance contracts,
where LEM experts from the supplier can monitor and report
on multiple assets from many companies. The in-house knowl-
edge of the motors is also a competitive advantage and a valu-
able resource, which is already being realised: Revenue FY 2016
for Siemens in software solutions and digital services combined
increased by 12% over the previous financial year, with revenues
of 3.3 Billion Euros in software and 1.0 Billion Euros for digital
services.
The key to a successful digitalisation business is the move
from data to information, which leads to insight, facilitates
action, and delivers impact [19]. The way gathered data and sen-
sors are used can vary greatly, but the overarching tenant of
moving from digital data to value is common. The new digi-
talisation business models are broken into four main trends that
exploit these opportunities: As-a-service business models, plat-
forms, intellectual-property-rights-based business models and
data-driven business models [19]. As-a-service business mod-
els for LEMs could be the opportunity to sell power or energy,
rather than the current hardware/software sale model. Critical
to this model is the data gathered from LEMs, as the relia-
bility improvement due to digitalisation is the value driver for
30 COSTELLO ET AL.
TABLE 2 LEMs in process stream [20]
Size (MW) Function
Motor #1 65 Gas refrigeration compression for LNG
Motor #2 65 Gas refrigeration compression for LNG
Motor #3 32 LNG boil off compressor
Motor #4 16 CO2compression
such a model. Both direct and indirect monetisation of data
are possible. The best example of direct monetisation of data
is Google where the search engine produces data as a primary
product, which is then further analysed for targeted advertising,
can result in a revenue stream. The LEM segment sits in the
second category: indirect monetisation. The data can be used to
improve the software systems or indeed the hardware itself. An
example of this is the “Digital Twin”, which allows for a digi-
tal simulation to be created to predict future behaviour of the
LEM.
This then has the potential to provide valuable feedback to
the manufacturing process: new materials, designs, and dimen-
sions can be considered digitally before any change to the man-
ufacturing process is considered. The data can also be used
to evaluate improvements to the software or analytics. Estima-
tions on temperature or vibration profiles, for example, can be
measured and evaluated with Big Data through machine learn-
ing algorithms to reduce the engineering tolerances required
in the software shutdown or monitoring system. This is then
a continuously improving feedback loop with the analytics
improving as more data under more varied conditions is
obtained.
There are clearly challenges associated with the gathering
of customer data. What is certain, however, is that the ben-
efits of digitalisation of LEMs need to be perceived to out-
weigh the risks of such a program, to allow the business plan
to succeed. In order to move from a hypothetical case to a con-
crete solution, the following section will detail a real-world case
study.
4.3 Case study
The target market for the initial implementation will be
assumed to be the O&G industry. This is a good basis for
studying follow-up markets, including electricity generation,
mining, and shipping. The O&G market has an existing knowl-
edge base comfortable with making risk-based decisions as
well as large value streams dependent on the reliability and
availability of the large drives in the compression drivetrain.
For example, a LEM has an approximate replacement cost of 2
Million Euro; however, the process in an LNG plant may have
a value of over 2.4 Million Euro per day. This makes the value
proposition compelling.
For this case study, data has been gathered for the Norwegian
Snøhvit LNG plant and used as the basis for the case study. The
plant has four LEMs, as described in Table 2.
4.3.1 Driving the process, the application of
LEMs
The 16-MW CO2compressor is used to re-inject produced CO2
back into the gas reservoir, which can serve the dual functions
of maintaining reservoir pressure in the gas field as well as a way
of storing CO2to reduce emissions to the atmosphere [21]. The
injection of CO2is an important part of the process, both finan-
cially and environmentally. However, due to complex reservoir
property interactions, CO2injection is not as time critical as the
other processes.
The 32-MW gas boil-off compressor takes the methane that
boils off as part of the process to maintain the low temperature
of the LNG and re-compresses and cools the gas back into the
liquid state at approximately –161 ◦C[22]. This compressor is,
therefore, a critical piece of equipment for the LNG process;
however, the complex interactions between the boil-off com-
pressor and process are outside of the scope of this study. For
this reason, the two 65-MW compressors will be the focus of
the quantitative section of the case study. These two compres-
sors are responsible for supplying the compression to feed the
LNG process and, therefore, can be directly linked to the pro-
cess availability.
4.3.2 Process value
The value of the LNG process is driven by the two LEMs #1
and #2 in Table 2and can far exceed the value of the individ-
ual machines. They are, therefore, critical pieces of equipment,
and the reliability and availability of this kit is, therefore, of high
value.
The Snøhvit LNG facility is estimated to produce approxi-
mately 4.3 million tonnes/year of LNG, 747,000t of condensate,
and 247,000t of LPG [21]. Taking the largest value stream of
LNG and assuming a conservative price of US$4.9/MMBtu
[23], a heating value of 53.38 MMBtu/tonne [24], with the
density of 451 kg/m3[24], gives 4.3*106x 53.38×4.9 =1.125
Billion US Dollars per year. This is approximately $3 million US
dollars per day or 2.4 million Euro per day. When compared to
the price of an individual 2 million Euro LEM, it is clear that the
value to the company lies in the process, not the motor itself.
The facility also contains storage in the form of two 125,000-
m³ LNG tanks, one 75,000-m³ condensate tank, and one
45,000-m³ LPG tank. This storage then equates to approxi-
mately 9.5 days of LNG production. This is significant in the
value calculation as it may, therefore, be possible to build up
LNG inventory in the tank in a planned maintenance schedule
to continually load the LNG transport during the plant outage.
This means 9.5 days of buffer in the planned maintenance case,
with 4.75 days assumed for unplanned maintenance (assume
average ullage of the tank at 50% full). This base case says that
for every unplanned incident, which is transferred to a planned
incident and conservatively holding all other variables constant,
the ullage factor alone could be valued at approximately 4×2.4
million Euro =9.6 million Euro. This shows that the assumed
duration reduction of downtime of one day assumed in the case
COSTELLO ET AL.31
FIGURE 4 Example gas well production (Own graph.)
study is likely to be conservative and would need to be updated
with specific data regarding the plant-specific and complex
interaction between customer, market, shipping availability,
tank ullage, and downtime.
4.3.3 Deferred production
Production outages in the O&G industry are often considered
deferred, rather than lost. This is because the hydrocarbon as a
product still exists and is likely just to be recovered later. O&G
reservoirs also exhibit transient pressure characteristics. This
means that a shut in well will produce at a higher rate after a
shutdown due to the reservoir pressure beginning to stabilise,
and the resultant near well-bore pressure is increased [25]. The
increased near well-bore pressure in turn increases the flowrate
and thus some of the “lost” production is quickly recovered.
An example of this behaviour is shown graphically in Figure 4.
This is dependent on complex reservoir and fluid properties and
interactions and requires highly complex reservoir simulations
to predict the precise behaviour.
For the trivial example above with a standard logarithmic
decline, despite the two days of lost production being equivalent
to 8% of the two months production (area “A”), the increased
rate after opening the well back up results in deferred produc-
tion of only approximately 1% (area “B” – area “A”) by the end
of the production period. This is significant as while the total
production volume produced does not change significantly, the
time value of money has a significant impact the further into the
future the production is pushed.
For the LNG processing case, the limiting factor is the LNG
plant capacity, rather than reservoir performance, and hence, a
linear production profile can be assumed. Thus, a production
deferral can be assumed to be the train capacity. An example
linear profile is shown in Figure 5.
The deferred production is assumed to be produced at the
end of life of the train, which may be some 30 years. In Fig-
ure 5, the deferred plant production A is produced in a much
shorter time in section B at the plants rated capacity. This is a
conservative position as it can also be argued that the plant end-
of-life is set, and the production value will indeed be lost, rather
than deferred. The NPV of the deferred production time can be
seen in Figure 6.
FIGURE 5 Example LNG plant production deferral (Own graph.)
FIGURE 6 Deferred production time versus NPV sensitivity (Own
graph.)
4.3.4 Value stream calculation
To justify to an O&G company the installation and implemen-
tation of a digitalisation package for LEMs, it is incumbent
upon the service provider to prove the justification of the
expenditure. To calculate this value, a tool was developed with
the following four stages: Background Data, Customer Input,
Sensitivity Analysis, and Decision Tree. The details of the tool
tabs are summarised in Table 3
4.3.5 Background data
The Background Data section allows the supplier to input the
required metadata, which is not viewable by the customer. The
onsite support cost, motor/drive/gear digitalisation options,
and the associated hardware are included. These options are
each assigned a recurring annual OPEX cost in Euro and a once
off upfront CAPEX cost.
4.3.6 Customer input
The Customer Input section is the first point for the cus-
tomer to begin inputting data (see Figure 7). Section A
contains data that pertains to the current customer installed
equipment and historical reliability data. The customer will be
required to retrieve this data from their computer maintenance
management system (CMMS) [26]. For new installs or for
companies lacking this data, the supplier will need to have
recommended generic inputs. This is especially important for
green-field analysis as historic CMMS data may only exist for
other service conditions, if at all.
32 COSTELLO ET AL.
TABLE 3 Digital package decision tool summary
Inputs Outputs Basis
Background Data Provider: Digitalisation OPEX / CAPEX
Onsite support costs
Calculates cumulative costs and savings Core data from supplier on digital
package
Customer Input Customer: Installed equipment, digital
package config, process and reliability data
Deferred production, direct costs and
savings
Data required from each process and
setup
Sensitivity Analysis None Sensitivities on variables Show which variables are significant
Decision Tree Customer: Split of responsible unit from
downtime
Best financial decision for digital
installation
Show which options to install
Own table
FIGURE 7 Customer input tab (Own sheet.)
Section B includes the assumed discount rate, the daily
deferred production rate, as well as the assumed years of pro-
duction deferral. These three inputs are utilised to calculate the
daily deferred production loss in Euro. This is a key input into
the final project NPV.
Section C requires the selection of digitalisation products,
depending on the size of the installed motors, drives, and gear
units. These dropdowns are drawn from the data table in the
Background Data tab and total the recurring OPEX and once-
off CAPEX cost.
Section D requires the final inputs with some of the key vari-
ables that provide the value case for the installation. As will be
seen in the following section, the avoided occurrence and dura-
tion productions losses are key metrics. These values will need
to be provided by the supplier as it is unlikely the customer will
have any reference values for such a system.
COSTELLO ET AL.33
FIGURE 8 Discount rate versus NPV sensitivity (Own graph.)
Finally, Section E is a simple summary of the costs of
installing the chosen digitalisation package and nominal savings.
The downtime split between units and the project NPV are cal-
culated in the decision tree tab, which is detailed in the decision
tree tab section.
4.3.7 Sensitivity analysis
Sensitivity analyses have been performed on the ten controllable
variables. The results can then be broken down into three sep-
arate groups: low-priority variables, supplier priority variables,
and customer priority variables.
The low-priority variables include number of tech support
calls, duration of tech support calls, and reduction of tech sup-
port calls. These three variables have a minimal impact on the
NPV of the project due to the relatively minor labour cost of
having a technician onsite. There is, therefore, minimal value in
the customer focusing attention on reducing the charge-out rate
or the duration or frequency of the call-out, which needs to be
treated separately here from the duration of the process outage,
which is a different value stream.
The variables that influence the project NPV and are pro-
vided by the supplier include the discount rate, number of
downtime incidents per year, the average downtime duration,
the deferred production time, and deferred production rate.
The discount rate is an assumption that can be made for the
future lifetime of the project and is not project specific. Because
of this, this variable may be an assumption by the supplier or
more likely, an existing internal number the customer uses in all
projects. This value will also depend on the project location and
country inflation rates.
It can be seen in Figure 8that the NPV of the project
increases non-linearly with an increasing discount rate and that
a conservative value of 2% has been used in this example.
As shown earlier in Figure 6, the deferred production time is
a near linear relationship, with loss increasing with time. A sim-
ilar linear relationship can be seen with the deferred production
rate, and the NPV value can be seen to be nearly directly propor-
tional to the value of the production stream, which is deferred.
Both the downtime duration and downtime incidence occur-
rences have a non-linear profile, and this data can be gathered
from the customers’ CMMS system or, for new installations, an
FIGURE 9 Downtime duration versus NPV sensitivity (Own graph.)
FIGURE 10 Reduction in downtime occurrences versus NPV sensitivity
(Own graph.)
estimate would be required from the supplier. The sensitivity
on the downtime duration term is affected by the percentage
avoided production losses; hence, an increase in downtime has
a larger, non-linear impact on the NPV, as seen in Figure 9.A
downtime incident of eight days has been assumed; however, it
is also clear that a failure requiring an unplanned replacement
of motor or drive would be considerably longer, possibly many
months. The best method for obtaining this value would be
through gathering of historical data from the customers’ CMMS
system.
Downtime occurrences are similar, with a plant seeing less
downtime indents obtaining less value from the digitalisation
package, as seen in Figure 10. An occurrence of 0.15 times per
year, or once every 6.6 years, has been assumed in the example
case.
4.3.8 Decision tree
The final data input area consists of two sections, the deci-
sion tree summary section and the decision tree, as shown in
Figures 11 and 12. The summary section required the input
split for the responsible unit. This breaks down the Savings
Attributed, CAPEX, and OPEX costs into its separate parts,
depending on which units have previously been selected in the
Background Data section. The graphical representation on the
right shows this breakdown. The Combined Costs are then cal-
culated for each possible permutation of drives, motors, and
gear unit digitalisation packages. This data is required to opti-
mise the digitalisation package install options in the decision
tree.
34 COSTELLO ET AL.
FIGURE 11 Decision tree summary (Own figure.)
The final decision tree calculation shown in Figure 12 is the
cumulation of all data and calculations in the sections Back-
ground Data, Customer Input, Sensitivity Analysis, and Deci-
sion Tree Summary.
In this case study, digitalisation on motors has an increased
value, also for the drives. As no gear units have been selected
in the Background Data tab, this is not included. The final rec-
ommendation is then included in both the Decision Tree and
Decision tree summary to “Choose Digital Package on Motors
and Drives”.
The most significant final calculation is the project NPV.
Here, the calculated NPV is over 1 million Euro when applying
the digitalisation package to both the electric motors and drives.
This analysis shows that digitalisation projects of large drives
can be justified when the true value behind the whole process is
considered. This can, therefore, form the basis of new business
models, focused not only on pure hardware sales, but other dig-
ital value streams as well. This has the potential to reinvigorate
the LEM industry by exploiting these market opportunities.
The summary of the multi-variable sensitivity analysis in
Table 4highlights the key variables that should be focused on,
for the customer and the supplier, for both green and brown
field installations. Discount rate has a high ease of data acquisi-
tion as the company will typically have an existing number used
through the company. Downtime incidents, downtime duration,
and deferred time are relatively easy to obtain in a brown-field
example as they should exist in the CMMS system; however,
green fields would need to rely on data from other installations
or companies. The deferred rate is typically a known value from
the process value in either green or brown fields. Tech-support
calls and duration are also easier to obtain from existing fields;
however, a good estimation from other locations is also possible
for green fields. The reduction in tech support callouts from a
digitalisation program is more uncertain; however, this has little
influence on the end project value. For the reduction in down-
time occurrences and avoided downtime, the data is more diffi-
cult to obtain as this is the change from the digitalisation instal-
lation. This data must be supplied by the supplier and is also of
high importance to the end project NPV.
In summary, the customer has three variables, which are of
high importance and a medium ease of data acquisition: Down-
time Incidents, Downtime Duration, and Deferred Time for
green field applications. The supplier should focus on the two
variables Reduction in Downtime Occurrences and Avoided
Downtime, which are of high importance and are difficult to
acquire for green fields, or medium difficulty for brown fields.
The importance on these variables shows that innovative busi-
ness models may be required to obtain this data: free or reduced
cost hardware, software, or analytics could be offered, for exam-
ple, to increase the data acquisition rate.
5 CONCLUSION AND OUTLOOK
The disappearance of over half the Fortune 500 companies
since 2000 due to digitalisation highlights the need for compa-
nies who produce LEMs to address this risk. The justification
of LEM digitalisation projects has, however, been a roadblock
to their implementation. The large investments from both the
O&G industry and the large drive industry show that they are
FIGURE 12 Decision tree (Own figure.)
COSTELLO ET AL.35
TABLE 4 Key variable sensitivity analysis
aware of the risk of ignoring the digitalisation movement, espe-
cially with the challenges of the low oil price in the O&G indus-
try. For this reason, this paper examined a path to find the opti-
mal degree of digitalisation of LEMs to deliver value to both the
customer and the supplier.
There are three distinct value cases for the customer: preven-
tative maintenance, improved forecasting, and other digital data
value. In the case study, the improved maintenance value stream
alone justifies a digitalisation project with a positive NPV. The
latter two have the potential to be significant value streams and
both require further investigation.
For the supplier, there are three value cases: hardware sales,
software sales, and leveraging data value. Whereas hardware
sales are the traditional value stream, software analytics and
recurring surveillance contracts have justified the digitalisation
project from the suppliers’ side in the case study.
Risk-based decisions, utilising both customer and supplier
data input into a decision tree tool, have shown that there are
seven variables with a high or medium impact on the project
NPV. Out of these, three variables are required from the cus-
tomer, which have both a high impact on a digitalisation project
NPV and are difficult to obtain: downtime incidents, down-
time duration, and deferred time. These should, therefore, be
a focus for the customer to reduce the uncertainty. This is espe-
cially important for green-field cases due to the lack of histor-
ical data and may require a closer collaboration with the sup-
plier and their world-wide fleet data. There are two variables
with high impact on the project NPV from the suppliers’ side,
which are difficult to obtain: reduction in downtime occur-
rences and avoided downtime. These should be a focus for the
supplier as they are significant for both green-field and brown-
field projects.
The extent of a digitalisation project can be assessed using
the tool created in this work, moving the business case away
from the traditional hardware sales to software, analytics, and
data value cases. This has the potential to improve businesses’
decisions, increase their revenue streams, and justify a digitalisa-
tion project.
The case study using LEMs in the Snøhvit LNG plant returns
an NPV value of over 1 million Euro over five years, while
including only preventative maintenance value and omitting
both improved forecasting and other digital value for simplicity.
Data should be gathered for the key variables to reduce the
uncertainty of the digitalisation project. This may require some
innovative product offers, such as free installations or reduced
OPEX costs for the customer. Further work is also required in
the other digitalisation value streams to fully exploit the digital
potential. This includes better probabilistic forecasting, better
quality gas contract data, and reduced inventory from the cus-
tomer’s side, as well as better hardware and software design and
improved analytics from the supplier side. Both the customer
and the supplier are also likely to benefit from other digital data
value streams, which have yet to be identified.
This paper shows that risk-based decisions can be used to
justify projects to digitalise LEMs, which satisfy both the cus-
tomers’ and suppliers’ value requirements. This has the poten-
tial to revolutionise the old LEM industry by opening significant
new value streams and bringing the industry into the digital age.
ACKNOWLEDGEMENTS
We acknowledge support by the Open Access Publication Fund
of TU Berlin.
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How to cite this article: Costello TJ, Strunz K,
Müller-Kirchenbauer J. Smart electric motor: Evaluation
of business potential for digitalisation in the large
electric motor industry. JEng. 2021;2021:25–36.
https://doi.org/10.1049/tje2.12008
