White Papers The Evolution of Smart Commodity Management | Page 5

Smart
 Commodity
 Management

 A
 ComTech
 Advisory
 Whitepaper

Of
 course,
 while
 all
 of
 this
 evolution
 and
 change
 was
 going
 on
 in
 the
 commodities
 markets,
 significant
 change
 was

also
  occurring
  in
  information
  processing
  and
  technologies.
  Vendors
  were
  able
  to
  leverage
  these
  newer

technologies,
  increasing
  processing
  speeds,
  improving

“The
 use
 of
 big
 data
 will
 become
 a
 key
 basis
 of

the
  user
  experience,
  and
  introducing
  greater

competition
 and
 growth
 for
 individual
 firms.
 From
 the

modularization
  of
  the
  applications
  -­‐
  allowing
  buyers

standpoint
 of
 competitiveness
 and
 the
 potential
 capture

to
 pick
 and
 choose
 just
 what
 they
 needed
 within
 the

of
 value,
 all
 companies
 need
 to
 take
 big
 data
 seriously.
 In

common
 CM
 footprint.
 The
 use
 of
 flexible
 tools
 meant

most
 industries,
 established
 competitors
 and
 new

that
  the
  software
  could
  be
  more
  configurable
  and

entrants
 alike
 will
 leverage
 data-­‐driven
 strategies
 to

personalizable,
  reducing
  implementation
  timescales

innovate,
 compete,
 and
 capture
 value
 from
 deep
 and
 up-­‐
and
  improving
  the
  users’
  interaction
  experience
  with

to-­‐real-­‐time
 information.”
 McKinsey
 &
 Co.,
 Big
 data:
 The

next
 frontier
 for
 innovation,
 competition,
 and
 productivity.

the
  software.
  These
  new
  technologies
  have
  also

allowed
  improved
  data
  visualization,
  increasing

productivity
  and
  enhanci ng
  risk
  management
  capabilities.
  Finally,
  newer
  architectures
  also
  improved
  the

connectivity
 of
 the
 CM
 solutions
 making
 it
 easier
 to
 interface
 or
 integrate
 with
 third-­‐party
 software.

Underlying
 all
 of
 this
 however
 is
 data.
 Back
 in
 the
 early
 days
 of
 natural
 gas
 marketing
 systems,
 all
 that
 was
 really

needed
 was
 daily
 volume
 and
 price
 data.
 As
 the
 industry
 evolved,
 more
 and
 more
 data
 was
 generated,
 stored
 and

processed.
  Simple
  data
  sets
  grew
  to
  incorporate
  massive
  time
  series
  data
  sets
  (and
  multiple
  versions
  of
  the
  same

data)
 for
 forward
 curves
 and
 expanded
 details
 for
 deals
 needed
 to
 be
 captured
 such
 as
 contract
 terms,
 complex

price
 structures,
 timelines,
 quality
 requirements,
 weights,
 and
 so
 on.
 Document
 images
 and
 other
 types
 of
 non-­‐
standard
  data
  were
  increasingly
  required
  to
  be
  stored,
  especially
  associated
  with
  scheduling
  and
  supply
  chains.

On
  the
  trading
  side,
  storage
  of
  phone
  recordings,
  IM
  logs
  and
  other
  evidence
  of
  negotiations
  became
  more

important
 as
 regulations
 and
 regulatory
 oversight
 increased.

 These
 regulations,
 such
 as
 Dodd
 Frank
 in
 the
 US
 and

EMIR
 and
 REMIT
 in
 Europe,
 have
 placed
 increased
 demands
 on
 the
 technologies
 of
 trading,
 requiring
 high
 levels

of
  transparency
  and
  reporting,
  including
  trade
  reporting
  to
  central
  repositories.
  Essentially,
  as
  the
  CM
  software

category
 expanded,
 evolved
 and
 matured,
 so
 did
 the
 data
 management
 requirements
 -­‐
 Big
 Data,
 both
 structured

and
 unstructured,
 needed
 to
 be
 better
 stored,
 manipulated
 and
 displayed.

Smart
 Commodity
 Management

A
  key
  requirement
  of
  Commodity
  Management
  is
  that
  it
  needs
  to
  assist
  users
  in
  making
  informed
  decisions
  by

transforming
  large
  amounts
  of
  data
  into
  insights
  by
  providing
  predictive
  and
  user
  controllable
  analytics.
  Smart

commodity
 management
 solutions
 must
 provide
 real-­‐time,
 meaningful
 and
 actionable
 information
 to
 traders,
 risk

managers
 and
 executives
 in
 order
 to
 enable
 them
 to
 view,
 analyze
 and
 simulate
 positions,
 market
 movements
 and

risk
  metrics.
 Users
 need
  to
  be
  able
  to
 monitor
 metrics
 like
 position,
 exposure,
  inventory,
 in-­‐transit
 movements,

scheduled
 movements,
 counterparty
 risk
 limits,
 P&L,
 and
 so
 on
 by
 both
 commodity
 and
 instrument
 type,
 across

both
 physicals
 and
 derivatives,
 enterprise-­‐wide
 or
 via
 drill-­‐down
 to
 trading
 unit
 and
 individual
 trader
 levels.
 Smart

Commodity
  Management
  must
  provide
  a
  wide
  range
  of
  tools
  to
  mine,
  analyze
  and
  generate
  insights
  from

transactional
 data,
 for
 use
 not
 only
 in
 strategic
 decision-­‐making
 by
 C-­‐level
 executives,
 but
 also
 by
 risk
 managers

and
 traders
 for
 tactical
 purposes.

Unfortunately,
  most
  of
  today’s
  CM
  software
  systems
  have
  a
  legacy
  of
  being
  built
  as
  client/server
  solutions,

focused
  primarily
  on
  capturing
  and
  storing
  transaction
  data.
  While
  they
  do
  provide
  some
  tools
  to
  analyze
  and

report
  that
  transactional
  data,
  these
  capabilities
  are
  by
  and
  large
  standard
  risk
  calculations
  (such
  as
  VAR)
  and

©
 Commodity
 Technology
 Advisory
 LLC,
 2014

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