Disruptive Technologies – A 2021 Update

Sponsored by
DISRUPTIVE
TECHNOLOGIES
RESEARCH AND REPORT
A 2021 UPDATE

INTRODUCTION AND BACKGROUND
In 2021, Commodity Technology Advisory LLC (ComTech) published its first Disruptive
Technologies research report (that version kindly sponsored by FIS). Technologies covered in
the study scope included cloud/SaaS, Artificial Intelligence (AI), Machine Learning (ML), big
data, automation and blockchain, amongst others. The findings were supported by an industry
survey that led to the broad conclusions that cloud/SaaS and data management initiatives were
in flight. AI, ML and automation seemed to be prepping for an explosion of use while blockchain
was overhyped and lagging. Over the last couple of years, our general market observations as
analysts have largely confirmed the results of the research.
In early 2021, ComTech decided that the whole topic
was worthy of a revisit as a result of many changes
and trends in the industry and so began a new
project. This report is the result of those efforts, and
it was kindly sponsored by Invensoft, Kyos, and
Commodities People.
The entire commodities complex is undergoing
significant change, driven at least in part by COVID
and the resulting lockdowns. Other drivers for change
like geopolitics, regulatory pressure, and increasing
emphasis on cost containment and improving
efficiencies in the physical side of the industry and
commodity supply chains, have been sharpened by
issues such as climate and sustainability. In turn, more
environmental, social and governance (ESG) aware
investors and consumers are also demanding and/or
driving changes. Trade finance is also included in this
mix of influences with the exit of many banks from the
space and the increasing reliance on innovative non-
traditional sources of financing. Just how were these
ongoing changes, challenges and trends impacting
the application of disruptive and other technologies
in the commodities industries?
Digitalization has been an ongoing trend within the
commodities complex for several years now and
it has been accelerated by many of the business
drivers noted above; and particularly so recently
with the requirement for commodities companies
to move to a work from home model – one that has
exposed any number of process and technology
weaknesses. In turn, digitalization has driven
continuing investments in various technologies such
as cloud, data management, workflow, automation,
AI and ML. Innovations associated with digitalization,
along with the other noted trends, also appear to
often be catalyzed by disruptive technologies. For
example, we now see AI being used to enhance
commodity price forecasts, which has in turn induced
several new providers leveraging AI to enter the
price forecasting market. Automation facilitated by
advanced technologies, particularly robotic process
automation (RPA), is also being adopted by vendors
and users, particularly to help guide and control
processes via a distributed/home-based workforce.

In all, the disruptive technologies survey had 63 valid
participants with just over half of those being in Europe
(Figure 1). Both the North America and Asia-Pacific
regions were also quite well represented.
Although technology providers were the largest group
of respondents (46%), end users of technology were
also well represented (38%), with the remainder
being consultants and systems integrators. The end
users also had diverse backgrounds, with respondents
representing an array of market segments including
producers, consumers, banks and funds, and
merchants (Figure 2).
When asked about how various technologies might
impact commodities and commodity trading over the
next 12-24 months, it was apparent that cloud and
SaaS may already be in the rear-view mirror. With
these technologies having been broadly adopted
and deployed across the industry, it’s perhaps not
surprising that these technologies would be seen as
having the largest impacts. Data management and
data mining, again somewhat mature and widely
accepted technologies, follow closely behind in levels
Disruptive Technologies – A 2021 Update A ComTechAdvisory Report
SURVEY RESULTS
ComTech conducted the survey component of this study between February 2021 and early June
2021 using a web-based tool. Survey responses were encouraged through email, blog and word
of mouth efforts. ComTech also partnered with Commodities People to include an invitation to
participate in the survey as industry players signed-up for their Commodity Trading Week event.
What impacts have these trends had on technology
adoption and investment? What are the use cases
that motivated the investment in, and adoption
of, these various technologies? These were the
questions our survey and research asked.
© Commodity Technology Advisory LLC, 2021, All Rights Reserved. 4

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of expected impact. AI and ML also ranked highly
as even though they are still somewhat early in their
adoption phase, several practical use cases have
emerged for these technologies. By comparison,
blockchain, despite much hype and investment over
the last 5 plus years, remains some ways off it seems,
ranking only slightly higher than opensource software
and virtual/augmented reality (Figure 3). Workflow
and microservices ranked somewhere in the middle;
however, if you combine ‘significant’ and ‘some’
impact responses, then they also show potential in our
respondent’s opinions.
Looking specifically at the responses of just the end
users shows little change versus the larger sample,
except for Microservices, which drop further down the
list.

By comparison to the earlier 2018 study, it seems more
respondents view all the technologies as having a more
significant/deeper impact now, yet the relative ranking
of those technologies remains nearly identical. AI and
ML are seen as more important in this study and, as
we predicted in 2017, appear to be front and center
now in terms of attracting both interest versus the other
newer/emerging technologies.
In terms of investment plans (Figure 6), cloud and
SaaS dominate with 84% of respondents saying their
firms expected to invest in those technologies. AI and
ML have moved up into the next place with almost
60% expecting to invest, while 32% expect to invest
in blockchain. These numbers remain pretty much
identical when just the end user group of respondents
is analyzed. However, comparison with the 2018 results
is interesting.
Comparing the results from the current survey to
those from 2018 indicates that investments in cloud
and/or SaaS (74% in 2018 vs. 84% in 2021) and
microservices (24% in 2018 vs. 41% in 2021) are
expected to increase. Investment in areas like AI and
ML and opensource will remain somewhat consistent
while investment in blockchain (46% in 2018 vs. 32%
in 2021), data management (66% vs. 56%) and VR/
AR (9% vs. 5%) might be expected to decline.
Blockchain appears to be struggling even more
in 2021 than it was in 2018 in the opinions of the
respondents. When asked where blockchain might
succeed commercially (Figure 7), there was little
confidence of its commercial application in any of
6

the tested areas except perhaps custody transfer /
ownership transfer.
Equally, using social media data draws little interest
for most respondents (Figure 8), with the number of
those who see value in mining social media for market
indications having dropped from 39% in 2018 to 25%
in 2021.

Referring to where disruptive technology will emerge
from among the various types of technology providers
(Figure 9), most expect it to emerge from the cloud-
based AI/ML vendors (Azure, AWS, etc.) and
technology platforms that facilitate digitalization of
deal negotiation, contract management and related
business processes. ERP providers are seen as
the least innovative in bringing new technologies to
market, trailing behind the other low-ranking segments,
including global IT firms (IBM, Oracle etc.), industry
consortia (Figure 9) and CTRM providers.
When it comes to which types of firms are at the
most risk of being disrupted by these emerging
technologies, the respondents pointed to CTRM and
ERP software firms as those most likely to impacted
(Figure 10).
Finally, as AI and ML are generating growing interest
across the commodities trading space, we asked where
these technologies might be applied for the most
impact (Figure 11). Perhaps not surprisingly, “Data
Analysis” is cited as the number one area for the use of
this technology. Asset Modeling, Price Forecasting and
to a lesser degree, Risk Management, are also all seen
as areas for potential use.
Commodities People often obtain ad hoc survey
data from its conference sessions audience as
well as collect data during registration for its
conferences. It’s data also shows that while AI and
ML seem to be seeing rapid deployment across
several use cases, blockchain has not yet fully
emerged commercially. A CTW tradeshow poll
showed that almost half of those responding felt AI to
be a game changer and almost 1/5th was most excited
by its potential. Though blockchain excites with its
potential (26% in that same poll), almost half of another
poll from the same conference suggest that it hasn’t
yet shown its business case convincingly in commodity
trading. These data points do help reinforce the trends
8

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In summary, the 2021 survey results show only subtle
yet arguably significant differences to the previous
survey conducted in 2018, but those differences are
informative. Cloud and SaaS seem to be the major
focus for most firms and these technologies are
clearly being broadly accepted within all sides of
the industry. It could be argued that digitalization
should be preceded by data management; elevating
data mining/management as a significant area of
investment, though there are hints that this focus is
lessening somewhat. Viewed another way, perhaps
there is an on-going shift from organizing data to
attempting to extract value from it?
Of course, at any point in time, each of these trends is
at a different level of maturity or adoption within any
individual company or technology vendor. While some
innovative vendors have already fully moved to a “true
cloud” platform, others for various reasons are only in
the early staging of architecting their products to run

effectively in a cloud environment. The same is true
with respect to end users. Though each respondent
may be at different points in the adoption curve for
any technology, the cumulative data does provide a
view of where the industry is generally with respect
to the technology adoption curve for the technologies
examined. As such, the data does suggest that we may
be close to the peak of cloud adoption and perhaps
slightly past the peak in terms of investments in data
management.
AI/ML and automation appear to still be prior to the
peak of the technology adoption curve and can be
expected to draw increasing levels of investment.
These technologies are finding some use in practical
business applications and new use cases are
emerging. Our research indicates this is driven in
large part by the need for better control of a remote
workforce, and the massive growth in the volume of
data generated and consumed by industry players
and the need for new tools/applications to extract
actionable insights from it. AI and ML empowered
Robotic Process Automation (RPA) tools are also
finding utility in allowing machines to manage routine
work and process tasks, which in turn frees humans
to focus their expertise on problematic exceptions.
Though use cases in commodity trading are still
somewhat limited in their scope, the early results
from these efforts show enough promise and return
on investment that we do expect to see increasing
interest and investment in these areas.
Blockchain, hyped and touted as “the next big thing”
for the past 5-plus years, still seems to be some way
off from broad practical application in the opinion of our
survey respondents. Indeed, it may be further off now
that it was thought to be in 2018. This is indicated not
just in the lower percentage of respondents planning
to invest in blockchain in our most recent survey, but
also by the apparent lack of use cases to support its
deployment. Based on levels of investment to date
and the results achieved, it seems that blockchain will
be likely be used initially to securely record certain
types of transactional data via smart contracts and
in managing custody transfer. We anticipate that
blockchain, given its inherent latency in capturing and
recording transactional data, will remain somewhat of
a niche technology deployed in a small percentage
of applications supporting after the fact recording of
commodity trading and management activity in the
near to mid-term future.
Aside from its established (though perhaps improving)
latency issues, an additional aspect of blockchain
that will likely hinder its uptake, is the need to
establish process rules, standards, and governance
for its use. In order to gain broad acceptance as an
enabling technology or platform, this often requires
the creation of a consortia of industry participants
to own and operate the initiative. Anyone familiar
with the history of commodities knows very well that
consortia, standards and working together do not have
a particularly successful track record in this industry,
particularly as the markets have increasingly become
more global in their reach. As indicated in Figure 9, the
survey respondents appear to echo this sentiment as
they do not consider consortia to be among the leading
candidates for promoting disruptive technologies.
10

Given that most blockchain initiatives to date in the
commodities industries have been driven via consortia,
their lack of broad success might be more a result of
their inability to overcome competing agendas in terms
of business processes and proposed standards, and
less so any shortcoming with the technology itself.
It is also interesting to examine perceptions as to where
the impetus for adoption of disruptive technologies is
coming from. Our respondents point squarely to those
technology vendors that are focusing on cloud delivery
of AI and ML tools, and to data and data management
solution providers. The availability of these platforms
that have embedded these technologies seems to be
driving additional adoption as opposed to users having
to invest in the basic technologies and develop their
own solutions from them.
Other technologies like VR remain on the edge of
perception it would seem; yet our research indicates
there does seem to be evidence that augmented reality
is getting closer to practical usage as there are a few
emerging use cases that will be covered later in this
report.

In summary, it appears the drive to digitalize, supported
by the broader trends of work-from-home, and the need
for greater efficiencies and improved control are driving
people’s perceptions of disruptive technologies. This in
turn makes investments in cloud, automation, AI and
ML, improved data analytics tools, and (based on events
in 2021) security more attractive or even pressing.
Further, when it comes to security considerations,
perhaps blockchain will have a future role by helping to
improve trust and verification, particularly in sensitive
areas such as critical infrastructure.
Of course, each technology has its own adoption
curve and there are early adopters and laggards
across the industry for each technology. However,
the overall push for adoption of these emerging and
advanced technologies does appear to be catalyzing
the movement away from monolithic systems to
ecosystems comprised of numerous specialized
applications; and, from traditional on premises to
SaaS or at least subscription priced. For example,
SAP’s S4/Hana and Industry cloud approach is a
somewhat dramatic shift for many market participants
who have relied on the company’s traditionally installed
ERP solution and may herald the ultimate end to any
continuing resistance to cloud solutions for mid and
large companies. Other examples of vendors that have
moved from being providers of traditional monolithic
applications to providers of cloud-enabled ecosystems
of specialized applications (or apps) include Eka
and Gen10. Even SAP has adopted the idea of an
ecosystem of web-based apps and other more recent
market entrants, including Previse Systems and
CTRMCubed, have made this their initial go-to-market
model. Still others, such as Beacon, have moved into
the space with a platform model which allows users to
leverage a sophisticated trading data model to build-out
those capabilities specific to their unique mix of assets,
processes, and markets.
It should be noted that despite the growing availability
and adoption of these emerging technologies, the
realityisthatcommodityfirmsareoftenlaggardsoverall
when compared to other markets such as financial
services or those that are more consumer oriented.
Given the complexities of producing, managing,
processing, trading, marketing and transforming
commodities, it is perhaps understandable that there
is still a long way to go in terms of digitalization and
broad adoption of these technologies in this space; and
why, for now at least, Excel remains the tool of choice
for many.

DISRUPTIVE TECHNOLOGIES IN THE
COMMODITY MARKETS
As the results of our survey indicate, there continues to be significant interest and investment
in many new and emerging disruptive technologies. Though we have included Cloud and SaaS
in our survey, the reality is that cloud-delivered software has now reached the point that it could
be considered mainstream and well past peak adoption.
Few companies operating today have not adopted
and/or are using at least one or more cloud/SaaS
applications - either by choice or by being essentially
“forced to” to operate effectively with industry
partners or service providers. Other of the examined
technologies, such as Blockchain, have been available
to industry participants for several years, but their
take-up as enablers of applications and processes in
everyday use by most industry participants have clearly
lagged the expectations that were held when they were

first introduced to the space.
In this section, we examine the current state of the
various disruptive technologies and use cases to which
they are being applied. This is in no way intended to
be an exhaustive listing of each vendor or product that
might fall under each technology category but is more
intended to provide a brief snapshot of the state of play
for each and provide a glimpse into how they are being
applied and a sense for their future potential.
Artificial Intelligence and Machine Learning
An excellent definition of AI can be found at IBM’s
website1
where AI is described as “enable(ing)
computers and machines to mimic the perception,
learning, problem-solving, and decision-making
capabilities of the human mind.” Machine learning is
also defined there as a branch of AI that “focuses on
applications that learn from experience and improve
their decision-making or predictive accuracy over
time.” Deep Learning is a type of ML in which the
application teaches itself to perform a task with
increasing accuracy without human intervention.
AI and ML use across the commodities complex seems
to have expanded rapidly in the last couple of years or
so. Often combined with workflow or automation, AI
and ML can form a later component of the digitalization
activities taking place across the industry or, in an ad
hoc mode, to help optimize specific activities, increase
efficiencies and reduce costs. In some instances,
it is used as a first pass attempt to automate routine
tasks or in data reconciliations, leaving exceptions for
human intervention. The move to this form of exception
management has obvious benefits in time savings and
improved process efficiencies, as well as making
better use of staff’s time.
Although by no means an exhaustive list, the following
use cases are among the most common examples
of the deployment of AI in commodities that we are
currently aware of.
Short-term market forecasting
In fact, demand and price forecasting is rapidly
becoming an overcrowded market with suppliers of AI
and ML enhanced short-term market data seemingly
cropping up everywhere. These vendors, by using
various sensing imagery such as arial and/or satellite
images, and bringing together other data including
macroeconomic, econometric, production and
inventories data, and even social media data, can offer
AI enhanced price forecasts across several classes
of commodities, ranging from agriculture to electric
1
https://www.ibm.com/cloud/learn/what-is-artificial-intelligence

power. Kyos also makes use of AI when tackling forward
curve models and to develop good hourly shapes.
Indeed, this is where it started using AI initially.
However, these AI enhanced forecasts do rely on
advanced data collection and the availability of key
data will vary across the commodities complex. For
example, satellite images can be useful for mapping
of mines and mine inventories for major metals
commodities produced in open-cast mines. A similar
approach can also be used in oil and petrochemicals
and agricultural production. However, for other
commodities where the amounts produced and
shipped are small, such tools may not offer much
utility, particularly if producers wish to conceal such
information by hiding stock or otherwise refusing to
cooperate with data collection efforts. Nonetheless,
even in markets or commodities with data gathering
limitations, artificial intelligence can be used to more
efficiently aggregate research, market sentiment, and
historical data to arrive at more accurate predictions
for commodity prices, estimate inventory drawdowns,
forecast vessel movements/port activities and so on.
An example of one company using AI tools to develop
improved forecasts is Enverus, the US-based data
services company. The firm is utilizing ML in their
short to mid-term price forecasting for power markets
and has demonstrated the value ML enabled tools
by apparently producing more accurate forecasts of
market prices than other sources and services during
the historic disruptions in the ERCOT power markets
in February of 2021.
Optimizing supply chains
Efficient movement of commodities through the
supply chain is crucial to profitability and is an area of
application for AI that can show a quantifiable return
on investment. An artificial intelligence enabled system
capable of consuming and optimizing real-time logistics
and pricing data can be a tremendous asset for these
companies, not only in managing supply chains, but
in helping make critical trading decisions. We have
seen many examples of the use of AI and ML in this
area including but not limited to the following functional
processes:
• Route optimization taking into consideration
transport mode, locations, costs, time, commodity
specifics like need for refrigeration, for example,
carbon footprint and so on,
• Automation of nominations, balancing and
scheduling using management by exception where
most transactions are scheduled automatically
while a smaller number of more difficult to manage
transactions are identified for manual set up or
checking,
• Gaining insights and greater visibility into supply
chains by using imagery of inventory and shipping
routes/ports to predict potential issues and/or
opportunities, and
• Reducing the complexity around supplier

Trading
The use of bots to perform automated and algo
trading is now quite common particularly in real-time,
intra-day power markets. However, this technology
has been in use across many other commodities and
markets to optimize and automate trading activities,
particularly in financially traded commodities. In fact,
an entire subcategory of ETRM software has emerged
to help automate short-term power and gas trading,
particularly in the European markets, specifically the
software allows the use of AI/ML to trade automatically
and often also to optimize asset dispatch. Vendors like
Volue (Likron), Trayport, eZ-nergy, FIS, Brady,
NavitaSoft, Igloo and others, are all marketing such
solutions. These solutions have also begun to extend
into scheduling and data visualization to support intra-
day trading activities. Other vendors like Enegen
and eZ-nergy have gone further and innovated asset
dispatch optimization solutions while others plan to do
so.
Venus Technology Ventures’ Mistro, created by
former traders, is another example of an AI enabled
solution in that that uses natural language processing
(NLP). The product is designed to understand
commonly used trading language terms and, with its
ML capabilities, learns quickly what types of deals a
trader would typically perform. Admins and/or users
can also add specific language used on their trade
floor into its dictionary. Once configured, the internal
dictionary picks up on keywords and phrases and
translates them into something that can be leveraged
for other processes and applications.
One advantage of incorporating AI solutions within
commodity trading is to help increase productivity and
save time through automated deal capture. Traders
often use multiple forms of communication—sometimes
simultaneously—to execute deals. Whether it’s through
messaging platforms, emails or phone calls, it can be
difficult for a trader to remember all the details of any
specific deal. AI enable NLP and ML systems can help
facilitate the process of gathering information from all
these sources and take appropriate actions based on
this information.
Techniques like NLP and ML can also be used to scan,
onboarding and collaboration using techniques like
anomaly detection, cycle time prediction and next-
best action recommendations.
One good example we have seen of employing
such technologies in supply chains is the initiative
by Polestar, Vasanda and Carbon Chain to
combine environmental, safety and governance (ESG)
auditability with supply chains optimization around
ESG impacts, including the carbon footprint of the
transaction. In this application, the Vasanda component
utilizes technologies to examine the ESG impact of
commodity production activities, for example, using a
variety of different data types including imagery.

listen, and watch across communications, social media,
and other sources to help provide early indications
of market imbalances to inform trade decisions, and
once acted upon, can help ensure more accurate trade
capture (many transactions are entered after the fact).
In this latter use case, it can also be used to help fill out
additional trade data based on an understanding of the
trader’s typical activities, reducing time spent entering
data.
Other Areas
There are many areas that AI can, and has, been
deployed in commodities. These include functions
like predictive maintenance, asset management and
optimization, inventory management, back office
for reconciliations, settlements, invoice matching,
and even in relatively obscure operational areas
like optimizing fish feeding and improving crop
management. SAP demonstrated a solution to
reconcile invoices at a recent Commodity Management
Forum that utilized AI, for example.
Increasingly,wealsoseeatrendtomanagebyexception
where processes are automated, and AI identifies those
where human intervention is needed. Event-driven
reporting, where events drive calculations, processes,
reporting aided via AI, is also quickly gaining traction.
Risk Edge has been developing AI based products for
some time and offers a couple of additional use cases.
For one Singapore-based Agri-business that operates
in over 70 countries, and supplies food and industrial
raw materials to over 22,000 customers worldwide,
Risk Edge delivered a web-based Planning & Analysis
(FP&A) solution. Their product utilizes advanced ML
algorithms to simulate its P&L and predict crop yields
based on historical data. In the metals and mining
industry, Risk Edge designed and built Journal Entry
anomaly detection solution using machine learning
algorithms and a statistical filter.
AI and automation have also been proposed and used
to help users navigate through multiple systems or
screens to complete working on various business
processes. This is particularly useful in entities that have
deployed multiple different solutions to support their
operations and where managing a business process
requires complex systems knowledge. As previously
mentioned, it can also be used to help automate and
manage by exception complex or data voluminous
processes and, help to migrate to event-driven solutions
whereby a specific event triggers a set of calculations
(e.g., valuation based on change in price).
Customer service is another where AI is proving
valuable. For example, as early as August 2015,
Shell launched an AI assistant for customers of its
lubricants business. Shell uses its avatars, Emma
and Ethan, to help customers discover products
using natural language. The Shell Virtual Assistant
functions through an online chat platform through

Robotic Process Automation (RPA) and Automated
Workflow
According to Investopedia, Robotic process automation
(RPA) refers to software that can be easily programmed
to do basic tasks across applications just as human
workersdo.Thesoftwarerobotcanbetaughtaworkflow
with multiple steps and applications. RPA software is
designed to reduce the burden of repetitive, simple
tasks on employees and is seen as having benefits such
as reduced costs and fewer errors. While there has
been significant interest in RPA for areas like financial
settlement, scheduling, risk management and so on,
ComTech hasn’t seen much evidence of its use beyond
automations of various aspects of workflow via a few
select RPA based tools. However, RPA and automated
workflows are taking off in many industries and as an
example, an Accenture study2
determined that over
43% of consumer-packaged goods (CPG) executives
indicate their firms already employ automation to cut
costs and promote efficiency.
the company’s website. Examples of information that
the system can provide include where lubricants are
available for purchase, range of available pack sizes and
general information regarding the technical properties
of specific products. The company has also reportedly
integrated a virtual assistant called Amelia into its
business model to respond to inquiries more efficiently
from suppliers regarding invoicing and it believes the
future of AI in its industry will see a significant increase
in unmanned and automated facilities.
Sinopec has a long-term plan to roll out construction of
10 ‘intelligent’ plants as part of an objective to achieve a
20 percent reduction in operation costs. In April 2017,
Huawei and Sinopec announced a collaborative effort
to design a “smart manufacturing platform.” AI is one of
eight core capabilities of the platform which will deliver
a centralized method of data management and support
integration of data across multiple applications used to
manage factory operations. AI would serve to establish
the rules and models that would inform how data is
interpreted and offer opportunities for improvement in
factory operations.
Kyos also uses ML in areas like renewables and PPAs
where it creates simulations for price with volumes of
production using ML. It also uses ML techniques to
look at historical data and project that forward, finding
ML to be a robust method to handle large quantities of
historical data efficiently.
AI and ML, especially combined with automation, do
appear to be attracting a great deal of interest and
investment and we do expect to see it deployed with
increasing frequency across all areas of a commodity
business.
2
https://www.accenture.com/t20170926T105943Z__w__/us-en/_acnmedia/PDF-61/Accenture-Strategy-Consumer-Goods-Workforce-POV.pdf

Though anyone who has ever worked in a commodity
trading organization or business unit knows that
there are many examples of highly repetitive and
labor-intensive areas that might seem to benefit from
automation and RPA, often these processes are
also prone to exceptions that would require frequent
human intervention. Nonetheless, it does seem that
trading could be an area is ripe for automation and the
widespread use of software robots.
The application of RPA first requires the business
process first be digitized. By looking for those highly
repetitive and labor-intensive areas of the business
process, opportunities for RPA can be identified and
acted upon. While this may include specific trade office
business processes, it can also be used to automate
application interoperability at the desktop and other
such activities.
Despite the fact the market continues to digitalize and
there is promise for additional adoption of automated
tools, Comtech’s research did not find massive uptake
of RPA in the industry. Further, it seems that some
of the early vendors interested in marketing to the
space have now largely backed away as projects have
been sparser than anticipated. Despite that, advanced
workflow solutions have found widespread adoption
usually as a part of other solutions like CTRM and CM.
Many CTRM and CM solutions now come complete
with programmable and configurable workflow and
approvals, and it has arguably become a standard
feature of this class of software.
Settlement Processes
Settlement is an area where RPA has been used and
one case was noted in a press announcement in late
2017 by Pioneer (now Hitachi) to announce that
it had joined forces with its longstanding customer
Essent, in a POC to apply RPA to settlement processes
in the Retail Energy Management group. “Innogy’s
Essent is focusing strongly on digitization in the energy
transition. For REM we accomplished that task with
TRMTracker, and its automation enables us to now
apply RPA, gain experience with the concept, and
prove the added value for Essent Finance,” said Pim
Flink, Senior Controller at Essent. “This is pioneering
work, and our results have been very promising in
how it will increase our operational excellence.” This is
now implemented and in production at Essent in the
areas of generating a settlement report and running
calculations where it saves manual work and frees up
workers to work on more creative activities.

RPA is often used in what may be termed a utility-type
function – much like that presented above, where it can
be used to assist a user navigate many systems when
managing common business processes. Some of the
most common examples include,
• Web Site Scraping
Energy and commodity firms have access to and
require a lot of data from a variety of sources like price
and weather services, SCADA and operational feeds
from various assets, inventory systems and so on.
Often much of this data is available on web sites but
accessing that information on a regular basis can be
a time-consuming process. RPA software can be used
to scrape those web sites on a regular basis (such
as finance-related trading sites, futures trading sites,
commodity trading sites, news media sites) and extract
specific information, summarize it and present it to
those who need it.
• Email processing
Assisting with the processing of large numbers of
incoming customer e-mail is an area where RPA can be
used to sort out the most common queries and route
them. It can also respond to the sender with an interim
message of receipt.
• Data Transfer
Using RPA to transfer data between different solutions
is another area of use that is in fact like helping users
navigate between different systems in order to do their
jobs more efficiently. A similar use case is to extract
data from paper forms using character recognition.
Other areas that show promise for RPA and workflow-
type tools are in a traditional back-office context as well
as in core business processes, particularly in retail
energy, such as:
• Automated tariff adjustment calculation and posting
• Customer onboarding – checking and validating
customer data
• Fast meter take over using automatic termination
with other providers
• Meter data and bill data processing – meter data
validation
• Order management
• Complaint’s resolution
Given the impacts of the pandemic and the resultant
work from home requirements, workflow and
automation has been increasingly used to provide an
extra level of control over key processes. Many CTRM
and CM vendors embed definable and customizable
workflow/approvals within their solutions to help users
model and manage business process administration
within the solution. This is then used to ensure that
critical activities are undertaken and completed by
users through the solution.
Utility Uses

Blockchain
There are new blockchain initiatives announced
almost daily, yet it doesn’t appear that many commercial
applications that rely wholly on blockchain are yet in
widespread use in the commodities industries. Further,
and particularly over the last year, there have also
been a growing number of more critical commentaries
noting an apparently growing belief in the industry that
blockchain has been over-hyped. In all, the jury is still
well and truly out as to if and when blockchain will find
its potential in energy and commodities.
Despite this ongoing negativity toward additional
investment in Blockchain, there has been no lack of
initiatives, consortia and POCs announced many of
which come to nothing. There are so many blockchain
initiatives targeting energy and commodities that it is
impossible to cover them all and so the narrative below
uses examples and is not a definitive list. There are
some notable blockchain efforts in the market like Vakt
and Komgo that have resulted in at least some adoption
in specific parts of the industry. However, when you
press Vakt about blockchain, its role in their solution
is suddenly diminished and is described as a just a
small aspect of the entire solution. In fact, Blockchain
appears to be less than 20% of the Vakt solution.
As previously noted, Blockchain requires standard-
ization and consortia (market agreement) to work
and to be attractive to new players - two things that
have historically not worked well in the commodities
markets:
1. There have been many standardization attempts in
the industry and none have really ever produced
what was intended, but rather ended up as a
compromise between a standard and extensions or
customizations. While there have been numerous
calls for greater standardization in the industry,
and with increased frequency, the fact remains
that standardization often gets in the way of
differentiation and the potential for competitive
advantage in the industry. As a result, it is often
given lip service and does not, in the end, result in
little more than some type of standard reporting.
However, as the industry seeks greater efficiencies
and reduced costs via digitalization and automation,
standards will need to emerge even if it means that
the competitive and innovative side of the business,
commodity trading (often where the true complexity
is) remains non-standard.
2. While there are many industry bodies and
affiliations, this has also been an industry in
which working as consortia has not proven to be
particularly successful either. This failure to reach
agreement is primarily because the establishment
of a blockchain platform really requires that each
participant gives up some control to a ‘central
authority.’ This is why most blockchain initiatives
have depended on a consortia structure in which
the overall solution is owned by an entity that is
subsequently co-owned by the participants. Again,
whether this model can really work in an industry
that has mostly been cutthroat, highly secretive and
competitive remains to be seen as the results of
such efforts to date have fallen well short of their
founding goals.

Inventory
Using blockchain to securely record ownership of
commodities is certainly an area where many initiatives
have already been announced. For example, the Dubai
Multi Commodities Centre (DMCC) announced plans
recently to create the first precious metals refinery and
storage facility in the Gulf region enabled by blockchain
technology. The facility will refine and store precious
metals including gold, silver, platinum, palladium and
rhodium, which will be optimized on goldexchange.com,
a secured trading platform. The refinery and storage
facility will create an optimized record of transactions,
making it possible to track all precious metals that are
refined and eventually sold internationally to more than
150 countries.
Trading, Trade Back Office and supply chain support
In commodities, one of the most visible initiatives is the
Vakt consortium. Using blockchain technology, Vakt
helps to manage physical oil transactions, eliminating
reconciliation and paper-based processes. It’s a
consortium of oil companies and banks (BP, Gunvor,
ABN-AMRO, Equinor, Koch, ING, Shell, Mercuria,
Chevron, Total, Reliance and Societe Generale)
focused on developing and deploying solutions for
post-trade processing. It has been designed to help
‘eliminate reconciliation and paper-based processes,
For blockchain initiatives to take off commercially,
they not only have to offer the promised business
efficiencies and benefits, but they also require new ways
of thinking and doing. It may be this that is impeding
real progress as much as anything now. Another issue
with blockchain is simply the hype. The simple mention
of blockchain has, in the past, created sufficient
market hype to inflate a public company’s stock price.
Unfortunately, knowing this, some companies have very
publicly announced their intentions to make significant
investments in the space, only to provide little or no
follow-up on the results of those efforts. This means
that parsing the various initiatives and announcements
needs to be done carefully with half an eye of the hype
involved. Unfortunately, it is still somewhat true that any
application that involves a hint of blockchain will likely
be marketed as a ‘blockchain’ application, leading,
even today, to out-sized press coverage and hype…
even if the blockchain aspect of it is nothing more than
window dressing. As such, in this market, it continues
to be buyer-beware!
All that said, Blockchain is still being proposed and
pursued for many use cases in the industry. Below
we mention a few of the use cases we are aware of.
Trying to categorize them is often an issue because the
blockchain initiatives also tend to claim they will extend
into a myriad of areas that often overlap with each other.

enhance efficiency and create new trade finance
opportunities.’ Recently, the platform went live for the
physically traded BFOET crude oil business and is
arguably the world’s first fully operational, enterprise-
grade blockchain platform to enter the market. Vakt
can also offer blockchain-based financing solutions to
its users via its alliance with komgo.
BHP Group has completed a first blockchain trade in
iron ore with China Baoshan Iron & Steel Co Ltd, in a
transaction worth around $14 million. BHP said that
the trial transaction using the MineHub platform was
worth about $14 million and was delivered in June. The
platform processed contract terms, exchanged digital
documents and provided real-time cargo visibility, it
added. The Minehub platform is designed to improve
efficiency in trading operations and environmental and
social governance (ESG) compliance in mining and
metals supply chains.
Another area where blockchain is being proposed is
in support of supply chain. For example, Trames, a
Singapore-based supply chain orchestration platform,
recently launched a software-as-a-service (SaaS)
solution that it claims will help small and medium
enterprises optimize supply chain operations and
improve overall profitability. Running on R3’s Corda,
the blockchain-based platform enables collaborative
drafting and confirmation of shipping documentation
and advanced analytics to help optimize decision-
making as well as speed up shipping processes by
reducing physical paperwork.
Another recent initiative in this use case area is the
Covantis blockchain platform. It caters for post-trade
processes and has moved into full production two years
after being created by a group of major agribusinesses
to digitalize agricultural shipping transactions. The
platform recently went live with more than 30 entities,
including – Archer Daniels Midland (ADM),
Bunge, Cargill, Cofco, Louis Dreyfus Company
and Viterra. It will initially cover bulk shipments of
commodities such as corn and soybeans from Brazil to
destinations globally, and will seek to connect shippers,
traders and charterers involved in these trade flows
and is aimed at eliminating post-trade paper-based
processes. After this initial use, Covantis intends to
bring in additional post-trade processes, including trade
confirmation, contract management, as well as laytime
calculation. The firm also has stated their intention to
explore other markets and commodities, including the
remaining grains and oilseeds, beyond the existing
offering of corn and soybeans.
TradeTrip, is another blockchain initiative that
involves digitalization of documents including Bills of
Lading, Certificates of Origin, Warehouse receipts
and Letters of Credit. TradeTrip markets the solution
as being designed to work via API’s with a client’s
existing ecosystem of commodity-focused applications
to provide greater transparency, trust and fewer
transactions around documentation through the supply
chain – particularly those documents around custody
transfer. The solution uses blockchain as well as 2D
QR codes to identify scanned documents and deep
learning-based text recognition to aid in transfer of
document data to systems.

GrainChain, is marketed as a blockchain-based
supply chain management firm seeking to enhance
visibility for producers and consumers in the commodity
supply chain in Latin America and North America.
The partnership is aimed at empowering suppliers
and farmers to protect their brand reputation through
adherence to supply chain standards in a bid to create
trust with the final consumers. The supply chain
management will be provided by the Mastercard
Provenance Solution, and together with GrainChain,
trusted end-to-end visibility will be given to products,
ranging from coffee to sorghum, from the farmers or
producers to the final consumers. This visibility will
afford everyone in the supply chain to track each
product and authenticate its origin, thus providing room
for trust in the industry.
Cargill, Agrocorp, Rabobank and other logistics
partners used a blockchain platform provided by
Singapore-based dltledgers to settle a $12 million
shipment agreement on April 1. The dltledgers
blockchain solution is built on the open-source
Hyperledger Fabric platform. The blockchain platform
provides a repeatable framework for end-to-end digital
trade executions, digitalizing the document and trade
execution process. The trade took a total of just five
days to settle, whereas traditional trading processes
can take up to a month. The platform created a shared,
unchangeable record of the transaction.
According to dltledgers, cross-border commodity
trading is one of the most promising sectors for
blockchain, which allows for real time monitoring by
multiple parties, dispenses with concerns about data
ownership, and simplifies the exchange of documents
in a digital, secure and decentralized manner. The
solution is a way to continue operations within the
global constraints of the pandemic.
Dutch bank Rabobank, commodities trader Concord
Resources, software developer Gen10, insurer
AON and warehousing firm PGS formed a blockchain
consortium and in November 2019 the group executed
a physical sale and repurchase (repo) trade using
blockchain. Repos are a type of financing, in which
the lender temporarily owns the goods instead of
getting a lien or charge over the assets. The original
asset owner repurchases the commodity at an agreed
time and slightly higher price. The consortium aims
to drive efficiency, traceability and transparency in
metals trading and repo financing using the blockchain
platform. It tracked the metal from the point of trade
to delivery and provided real-time confirmation of
completion without manual intervention. The trade
took less than five minutes to complete using a mobile
phone.
Perlin, large companies in Singapore, and the
government have teamed up to develop an open-
sourced blockchain trade platform (ICC TradeFlow
platform). DBS Bank and Trafigura, in collaboration
with Infocomm Media Development Authority
(IMDA), the International Chamber of Commerce
(ICC), Enterprise Singapore, and Tradetech
Perlin aim to connect trade partners across borders
and streamline manual paper-based trade processes
to improve trade flows. Built on IMDA’s TradeTrust
network infrastructure and powered by Perlin’s

blockchain technology, the platform is designed to
be interoperable with existing and future digital trade
platforms.
The Ricex platform is a blockchain-based rice
trading exchange developed by Fujitsu. The platform
is designed to specifically address the problem of
managing trading across a wide variety of strains, each
with differing quality, taste and fragrance, all of which
determines the price of rice. The Ricex digitalizes rice
trade using blockchain and provides a transparent
ecosystem of importers, exporters, wholesalers,
producers, regulators and other stakeholders. The
platform claims to streamline the trading process by
providing insurance, shipping information, inspections,
and automated settlement to participants. With the
blockchain platform, traceability is also enabled,
allowing buyers to identify rice certified as sustainably
grown.
Even traditional CTRM is seen as a candidate for
blockchain and EnHelix®, an oil and gas commodity
trading and logistics management software marketed
as having embedded artificial intelligence and
blockchain capabilities, recently announced its
Marketplace Blockchain software for commodity
trading companies. The company claims its software is
an advanced blockchain solution for trading companies
which helps to standardize and automate its system of
physical trades. They note they believe their solutions
reduces fraud and operational risks and expedites
payment and provides other benefits like automated
trade execution via a distributed blockchain enabled
application to reduce trade discrepancies and can
integrate with existing ETRM systems.
Another trading area that has been touted as ideal for
blockchain is at the retail end of power and gas trading.
Distributed power generation and small local grids
which can function independently have been identified
as an area for blockchain to provide peer-to-peer online
platforms to trade energy. Various initiatives exist in
this space including one by Transactive Grid in April
launched a peer-to-peer (P2P) transaction platform
that enables Brooklyn Microgrid members to buy and
sell electricity produced from residential PV solar
installations. Other examples include Germany’s GridX
and Australia’s Decentralized Energy Exchange
(deX) and numerous other examples exist and this
area does seem ideal for blockchain.
Finally, TradeCloud is an initiative started by several
ex-commodity traders in Switzerland. Its initial focus was
on a communications platform for metals commercial
users and, based on the success of developing that,
a similar platform for energies like coal, oil, refined
products, and so on. It has also released a post-trade
component that utilizes the blockchain and intends
to connect to a wide range of third-party services and
develop an API to connect to commercial CTRMs prior
to the end of the year.
In trade negotiation, the solution uses several setup
configurations like a workflow application to allow
appropriate communications, and a number of other
setup and configuration items to define various contract
terms and conditions. Users can set up sales and
purchase proposals and share them as broadly or as

narrowly as they wish while others can explore the sort
of deal, they may be interested in. Once both sides of
a deal are moving forward, each step is recorded and
captured whether it be a contract or deal changes
via conversation or edits and it is all done in a way to
highlight what has changed and what needs to get
done next. Once accepted, a legally binding contract is
created. The solution also covers the KYC process in a
similar way such that parties can exchange documents
and go through an organized process securely and
have a complete audit trail at the end of that process.
The post-trade aspect also has a bit of permissioning
and set up to configure of course though nothing so
very complex. It seems straightforward to achieve this.
The trade is then imported, and the system shows the
work that each person and organization need to do
following it through to completion. Amendments can
be made to the contract, parties notified of events like
insurers, warehouses, logistics firms, banks, and so
on and everything is auto filed as the processes are
worked through. All are written to the blockchain for
security, trust, and confidence. The highly complex and
often manual steps are digitalized as a result.
Sourcing
The Responsible Sourcing Blockchain Network
(RSBN) is a network aimed at supporting responsible
sourcing and production practices from mine to market.
Several mining and metals producers have joined the
platform to participate in a circular value chain, tracing
commodity flows in near real time as well as replacing
cumbersome paperwork. Built on IBM Blockchain
technology and powered by the Linux Foundation’s
Hyperledger Fabric, the RSBN platform aims to
improve transparency in the mineral supply chain by
providing a highly secure and immutable record that
can be shared with specified members of the network.
RSBN is designed to be adopted across industries
by original equipment manufacturers (OEMs) in
automotive, electronics, aerospace and defense, as
well as their supply chain partners such as mining
companies and battery manufacturers.
Atomyze LLC is the US branch of a global fintech
platform that claims to digitalize and modernize
the trading experience for products, assets and
commodities by enabling investors to invest in the
actual underlying commodity directly and safely. Using
technology powered by blockchain, Atomyze provides
a marketplace for investors to buy digitized assets of
any kind, including commodities, for issuers to trade
their assets on blockchain, and for the management
of supply chain contracts between issuers and
commercial users.

Trade Finance
Komgo is a blockchain trade finance platform
specifically targeting commodities in which Rabobank
is a member alongside other prominent Dutch banks
ING and ABN Amro and several other international
financial institutions. To date, the focus of the initiative
has been on oil and gas. The platform is said to provide
a wide range of solutions that facilitate trade finance
between participants and increase the speed of
transactions. According to the company, the blockchain
technology allows stakeholders to control a deal in real
time and offers improved transactional transparency.
Currently the company offer two products: 1) a digital
letter of credit (LC), which allows commodity players
to submit digital trade data and documents to their
banks, and 2) a know-your-customer (KYC) solution
to standardize and facilitate the KYC process enabling
exchange of documents on a “need to know basis”
without a central database.
dltledgers has also launched a platform called SmartFin
for small and medium enterprises (SMEs) to access
trade financing from alternative financiers. SmartFin is
a blockchain-based system that matches SMEs that are
seekingfinancetofunderswhocanprovideit.According
to dltledgers, over 400 companies have already signed
up, and can currently access financing from a handful of
non-bank financiers, including TradeFlow, Drip Capital,
Funding Societies, Crowd-Genie, Stenn International
and Capital Match.
Document Management
TradeTrip is a blockchain initiative that involves
digitalization of documents. Currently those documents
includeBillsofLading,CertificatesofOrigin,Warehouse
receipts and Letters of Credit. More document types
will be added. The TradeTrip solution is designed to
work via API’s with a clients existing ecosystem of
commodity-focused applications to provide greater
transparency, trust and fewer transactions around
documentation through the supply chain – particularly
those documents around custody transfer. The
solution uses blockchain as well as 2D QR codes to
identify scanned documents and deep learning-based
text recognition to aid in transfer of document data
to systems. Currently the solution is being piloted
by a company in Singapore and is part of a couple of
accelerators in Singapore as well. The company sees
the solution growing not just in terms of documents
but also in terms of tracking cargoes and via the use
of smart contracts, which could facilitate event-based
activities like automating a payment on receipt of cargo.

Will Blockchain Break Through?
There are any number of initiatives around blockchain
and new ones are announced almost weekly. Though
this level of activity has been going on now for 3-4 years,
we still see very little blockchain being used in practical
application across the broader commodity markets.
And in fact, the murmurs of discontent and projects
that never really took off far outnumber the applications
that could be viewed as successful. A recent third-
party review3
of 33 blockchain projects involving large
companies announced over the last 4-years showed
that at least a dozen of these projects, which involve
major banks, exchanges and technology firms, have
not gone beyond the testing phase, and those that
have made it past that stage are yet to see extensive
usage. In 2019, Lauren Manning, founder of GCTA, an
organization found to improve the trading process for
the commodities industry through the adoption and
standardization of technology, said she felt blockchain
was at least a decade away4
even while touting it for
many processes in commodities. And, despite the
various initiatives previously noted, there are truly few
commercial applications in widespread use.
That said, blockchain is likely poised for further
adoption, those it’s broad use may on be in certain
application areas, such as an adjunct for clearing and
settlement process. However, for even limited usage
to occur, transaction speeds and power requirements
likely must continue to improve.
Blockchain will need to move beyond the hype to the
point where it is a technology that has a real business
case, and its use offers superior results when compared
to competing and more mature technologies. Moving
from hype to reality, that point when Blockchain can
quantifiably demonstrate its true value as a broad
solution in a complex market will take some time. In our
opinion, widespread use of blockchain remains years
away.
Augmented and Virtual Reality
Despite the lack of interest in these technologies
within our survey, continuing investment in applications
and equipment across various industries, and
investigative spending within some segments of
commodities industries indicate they do hold some
potential to have an impact in the coming years.
For example, Citi released a proof of concept5
for
an augmented reality trading application for stock
trading, back in early 2019. Using the Microsoft
HoloLens headset, Citi worked with a virtual reality
design firm and the results can be seen in a You
Tube demo where a Citi trader “Jason” checks the
3
https://www.reuters.com/article/us-blockchain-finance-focus-idUSKCN1UB0YV
4
https://agfundernews.com/the-lunacy-of-blockchain-drove-commodities-veterans-to-launch-new-technology-association.html
5
https://qz.com/650872/citigroup-wants-to-bring-microsofts-hololens-and-augmented-reality-to-stock-trading/

news on the traditional 2D monitors. It features voice
commands and holograms of other traders. “The
trader is no longer confined to the trading desk and
could project graphs and maps the size of the room
in the AR space, enabling the more intuitive input and
analysis of information,” according to Citi GPS in the
October 2016 report6
. In that report, Citi predicted
that VR/AR will be used extensively across a range
of applications with growth in VR/AR equipment,
content and services going from $80bn in 2020 to
$569bn in 2025.
Banks maybe moving forward with the technology.
Millennials are more accepting of the technology and
a growing part of the workforce. However, the trading
floor application may yet be some way off. However,
there are several potential use cases emerging.
Virtual Desktops
Traders do like a lot of screens and devices, and the
ability to put that in a VR office to have more space
maybe attractive. In 2017, FlexTrade demonstrated
an augmented reality trading application with Microsoft
HoloLens for FX. Using existing technology and
application programming interfaces (APIs) within
its EMS software7
, its FlexAR application offers an
interactive order blotter, trade ticket and charting in the
virtual space. However, while initially delivered as VR
it was found that this made it very difficult to work by
cutting off the outside world and so the application was
moved into augmented reality where it worked much
better. The trader uses hand gestures or key words to
interact with virtual objects.
AI can be combined with VR/AR to add in contextual
information and for image recognition. Realistically, the
technology is some way off, but it may find use in some
of the following areas.
Data Visualization
Being able to visualize ever increasing types and
volumes of data is a continuing and growing issue and
AR and VR may make it easier and faster to visualize
and organize large amounts of data. Today, for example,
Salesforce uses Oculus Rift to create an immersive
3D environment for analyzing data and Fidelity Labs, a
part of Fidelity Investments, has also taken advantage
of the technology behind Oculus Rift.
In commodities related industries, the oil and gas
exploration space has been the earliest and longest
running example of a practical use of data visualization
6
https://www.citi.com/commercialbank/insights/assets/docs/virtual-and-augmented-reality.pdf
7
https://flextrade.com/buy-side-flextrade-products/

Trading
As mentioned, although this may be some way in the
future, the ability to better organize vast numbers
of screens and data, and conduct business with
holographic versions of counterparties may well be one
area where this technology takes off, and most likely
in the financial and equities markets. However, there is
little demonstrable progress in this area in the physical
commodities markets.
using VR/AR. Dating back to the 1980’s, service
companies such as Halliburton and E&P companies
such as Exxon, ARCO and many other majors have
employed data visualization rooms, or immersive
environments, to enable better collaborative visualiz-
ations and interpretation of 3d and 4d seismic data.
Though some of these environments are still in use
today, advances in processing power and desktop
capabilities have moved most of these data visualization
techniques onto desktop machines and large display
screens that utilize active 3D glasses worn by users.
Other uses of 3D visualizations being used today
include mine planning, stockyard management for bulk
commodities (such as that available from Eka’s bulk
terminal operations system) and modeling complex
processing facilities for planning and operations.
In commodities trading, simple 3D visualizations are
usedtodayformodelingvolatilitysurfacesforanalyzing
price and market risks – a capability embedded in a
few commercially available CTRM solutions.
Training & Education
This type of use is probably the most obvious as both
VR and ER technologies are in use in industries such
as airlines for pilot training, oil field services firms for
equipment training, and various other industries where
training on large industrial equipment can be difficult,
expensive or pose HSE risks. That said, there are yet to
be any high value use cases for training identified in a
commodity trading-type environment.

DISRUPTIVE TECHNOLOGIES AND THE
IMPACT ON CTRM AND CM
It could be argued that the pace and level of change in commodities related industries is
unprecedented and the technologies that enable is market is both driving and being driven
by those changes. In the end, the industry is evolving towards a more integrated supply chain
orientation with an emphasis on optimization, collaboration, efficiency and better controls, and
all of this does and will continue to impact CTRM and CM software.
As analysts, we have been observing the move away
from large monolithic and siloed applications towards
ecosystems of more agile and focused applications
that use rich API’s to tie them together. Increasingly,
these specialized applications are being deployed in
the cloud and on cloud infrastructures that provides
controllable scalability and improved performance and
security. Microservices are increasingly being used
within this context.
Digitalization is also helping with the elimination of
the more obvious manual repetitive processes that
plague the industry. Increasingly workflow solutions,
RPA, AI and ML are also being deployed to ensure
process flow adherence, better operational controls
and more streamlining by reduction/automation
of common manual processes, with provisions for
better identification of exceptions requiring human
intervention. We also note a trend towards event-driven
calculations and processes.
Yet another area of CTRM/CM impact is in enhancing
the user experience and provision of advanced data
visualization. While VR and augmented reality may still
be a way off in terms of everyday use on the trading
floor, innovating better ways to view and analyze data
is increasingly becoming a key attribute of the modern
CTRM and CM solution. Some vendors, such as FIS,
have included 3D visualizations of risk surfaces as part
of their native capabilities, more often advanced risk
analysis techniques have been accomplished via data
integration to solutions such as Matlab which provide
more advanced and powerful data visualization tools
and capabilities than one might find in most CTRM
solutions currently available.
Natural language processing (NLP) is being used today
in some CTRM solutions, such as Molecule’s ETRM/
CTRM platform, to enable users to more quickly capture
deal data. Though perhaps some way off, we may see
other, potentially more efficient ways of interacting
with CTRM software made available, such as use of
voice commands supported by AI. AI and advanced
workflow automation may also be used to better enable
users to more efficiently navigate multiple screens and
applications in order to complete tasks and activities.

AIhasalsodemonstratedutilityindocumentprocessing
in the commodity trading space, though its value to date
has been limited given the highly structured nature
of most physical/electronic documents exchanged
in these markets. Nonetheless, as more companies
continue to move to a fully digital environment, we might
expect to see greater use of AI-enabled document
management as a native CTRM function.
It is unlikely that blockchain will ever be embedded
as a native capability in any CTRM/CM application,
though certainly as blockchain solutions gain
traction in energy and commodities, CTRM/CM will
need to interface with them. The most likely points
of interaction will be in blockchain managed custody
transfer records, smart contracts, and aspects of
settlement where confidence and trust are key.
Given the increasing market interest in many of the
disruptive technologies examined in our research, and
their use cases to date, it is clear is that the future
of CTRM and CM must be one of innovation and
improvement from architecture to user experience.
This will certainly pose a challenge for both smaller,
niche vendors who lack access to funds as well as to
larger vendors with legacy software and large installed
bases that must be managed, guided and moved
through iterations of complex software that supports
years of business-critical information. Newer vendors
will undoubtedly hold something of an advantage
relative to being able to leverage these technologies,
but they will probably lack depth of functionality,
financial security and brand recognition possessed by
the larger legacy CTRM/CM providers. That said, if
the future really is ecosystems of solutions, perhaps
there is room for all in best of breed type ecosystems
constructed by the users themselves, one in which
these emerging technologies can live alongside the
narrowly focused but functionally deep traditional
solutions and agile components?

Invensoft Technologies is an IT Company providing Com-
modity Management Software Solutions for business
in Agricultural Commodities, Metals, Minerals and Con-
centrates. Since year 2000 Invensoft is supporting top
global commodities companies including Fortune 500
companies in Asia, Africa and Latin America covering In-
dia, Indonesia, Singapore, Malaysia, Vietnam, Kenya, Tan-
zania, Uganda, Ivory Coast, USA, United Kingdom, Peru,
Mexico, Honduras and Colombia.
Invensoft-XBS© popularly called as XBS is a Unified
Commodity Management Platform offering Commodity
Management (CM), 3rd Party Commodity Warehousing
& 3rd Party Logistics (CW3PL) and Commodity Trade
Risk Management (CTRM) covering End-to-End needs of
Commodity Business. XBS is an enterprise Web App with
an integrated Mobile App called XBS Apps© available on
Apple iPhone, iPad, Android Mobile phone and Tablets,
offers users seamless operational, reporting experience
with business controls on real time.
Integrated with Digital technology and AI, our solutions
transform the commodity business to the next genera-
tion digital business. Starting from commodity purchase
throughout the life cycle including warehousing, process-
ing and logistics, Digital technology and AI has been inte-
grated to enable intelligent traceability even after multi-
ple cycles of blending and bulking. AI based solutions are
available for purchase price intelligence, quality analysis,
processing yield prediction and QR code based smart
warehousing of commodities.
XBSCloud is an End-To-End Cloud SaaS offering by In-
vensoft providing Web Apps, Mobile App, including ser-
viced and self-serviced BI with no up-front license fees,
lower initial and operating costs. Subscription fees and
implementation are tailored to fit every business ranging
from 5 user companies to 500++ enterprises. Contact
less and remote implementation enables go-live in few
weeks. XBSCloud offering includes:
• Invensoft-XBS© CM provides extensive features
with out of the box localization for Commodity Ori-
gin Business, Procurement, Quality Management,
Processing, Warehousing, Certification, End-to-End
Bi-Directional Traceability, Sustainability Certifica-
tion, Trading, Hedging, Risk Management, Positions,
Exposures, Daily P&L and reporting.
• Invensoft-XBS© CW3PL provides innovative features
for commodity 3rd party warehousing, processing
services and logistics business covering commodity
intake, quality management, warehousing, collateral
management, processing, packing, container stuffing
and shipping. Innovative Customer Center module
enables customers, bankers to be a part of business
process.
• Invensoft-XBS© CTRM provides end to end features
for commodity trade risk management involving in
Contract Management, Trade Management, Ware-
housing, Traceability, Hedging, Risk Management,
Positions, Exposures, Daily P&L and reporting.
• XBS APP© is an integrated Mobile App for a seam-
less commodity business management on iPhone
and Android devices. Users can access real time in-
formation on inventory, position, intakes, shipments
and approve purchases and sales contracts on the go.
Website: www.invensoft.co.in
ABOUT INVENSOFT

ABOUT KYOS ENERGY ANALYTICS
KYOS offers analytical services to energy market play-
ers. Since its establishment in 2008, KYOS has de-
veloped software for power, gas and other commodity
markets. To support day-to-day operations, our analyt-
ical software combines innovative mathematical solu-
tions with a user-friendly interface. Furthermore, KYOS
supports energy market players by performing advisory
projects, mainly focusing on flexibility questions.
Based on a solid analytical framework, KYOS offers:
• Software to optimize, value and manage flexible en-
ergy portfolios
• Advisory services to support decisions in energy
markets
• Training courses for energy professionals
KYOS’ main software solutions are focusing on identify-
ing and valuing the flexibility of the assets of our clients:
• KyPlant: valuation and optimization of power plant
assets
• KyStore: valuation and optimization of gas storage
assets
• KySwing: valuation and optimization of swing con-
tracts
• KyPPA: valuation and risk management of renew-
able PPAs
KYOS offers expert in-depth knowledge on identifying,
unlocking and hedging the value of flexibility of energy
assets and is widely used by clients in various adviso-
ry projects. Projects include advisory work for national
regulators, major electricity producers, project develop-
ers and utilities. Topics range from risk assessment of
power purchase agreements for new wind generation
projects, impact of transport costs on gas storage eco-
nomics, to expert views during arbitration cases on the
value of gas storages.
KYOS’ clients can be found across Europe, most nota-
bly in the UK, Ireland, Germany, France and the Neth-
erlands. In addition, our services are used in Japan. We
also serve major players in the commodity markets with
our PRM (Portfolio and Risk Management) System.

Since 2009 we’ve worked across all facets of global
trading, creating an effective global communication
and information channel for everyone involved in the
world’s Commodity and Energy sectors. We bring the
right people together, for networking, knowledge-ex-
change and doing business.
Commodities touch every single person on the planet,
and we communicate to everyone in the production,
trading, transporting and consuming of commodities
and energy – from a simple coffee bean, to rocket fuel,
to alternative energy.
We provide the vital physical and online forum for the
participants in energy and commodity markets to ex-
change ideas and knowledge, find out about innova-
tions in their market sectors, and learn from market
experts.
Our clients value our independence, understanding of
the complexities of commodity and energy trading and
the way we bring the community together.
Keen to learn more?
Drop us a line on ben@commoditiespeople.com
ABOUT COMMODITIES PEOPLE

ABOUT BEACON
The Beacon Platform is the only commodity and ener-
gy platform to provide access to all source code within
an integrated development environment, together with
front-to-back trading and risk management applica-
tions. Beacon was built by the team that created last-
ing trading and risk technologies at some of the world’s
largest banks. Their technology platforms have stood
the test of time at the world’s premier financial institu-
tions. Beacon makes the same level of technology avail-
able to everyone. Beacon brings together two of the
lead engineers and co-founders, Kirat Singh and Mark
Higgins, who are responsible for the three premier en-
terprise innovation platforms in the industry. The Bea-
con technology builds on nearly 30 years of innovation,
refinements, and lessons learned at Goldman Sachs,
JPMorgan, and Bank of America and Merrill Lynch.
Beacon’s transparent source gives developers access
to the entire Beacon platform which enables the client
–- not the vendor –- to own the full technology stack. It’s
designed to be extended, not replaced. Beacon’s flex-
ibility makes commodity firms more nimble. Beacon’s
development environment enables firms to centralize
all their technology and development on one platform;
develop, deploy and use new strategies and applica-
tions quickly; and adapt to regulations faster than be-
fore. This transparency and flexibility empower clients
to securely build, iteratively test, tweak and integrate
proprietary models and analytics, and to extend end-us-
er applications easily. Beacon takes away the shared
pain of implementing new technology and lets firms fo-
cus on their own core activities. The future of commod-
ity trading is being built, hosted, and distributed on the
cloud. Beacon delivers the benefits of cloud technology
to clients, including cybersecurity, flexibility, and scale.
Beacon helps to solve the build vs. buy challenge. Cli-
ents can manage technology infrastructure as well as
build their own solutions in one secure environment.

ABOUT
Commodity
Technology
Advisory
LLC
Commodity Technology Advisory is the leading analyst organization covering the ETRM and
CTRM markets. We provide the invaluable insights into the issues and trends affecting the
users and providers of the technologies that are crucial for success in the constantly evolving
global commodities markets.
Patrick Reames and Gary Vasey head our team, whose combined 60-plus years in the energy
and commodities markets, provides depth of understanding of the market and its issues that is
unmatched and unrivaled by any analyst group.
For more information, please visit:
www.comtechadvisory.com
ComTech Advisory also hosts the CTRMCenter, your online portal with news and views about
commodity markets and technology as well as a comprehensive online directory of software
and services providers.
Please visit the CTRMCenter at:
www.ctrmcenter.com
19901 Southwest Freeway
Sugar Land TX 77479
+1 281 207 5412
Prague, Czech Republic
+420 775 718 112
ComTechAdvisory.com
Email: info@comtechadvisory.com

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