Embedded Analytics
Makes Routine Decision-Making Automatic
2011
MercuryGate International, Inc.
www.mercurygate.com
TMS that delivers.
Business rules based on stale information can lead to costly decisions! Business Intelligence
results from extracting and analyzing historical information, then summarizing specific information for decision-making in the future (decision-support). Theoretically, you make better
decisions on future instances of a subject by analyzing previous instances, summarizing specific attributes, and incenting positive performance or discouraging negative performance,
capitalizing on what you learned from the past.
Most Transportation Management Systems (TMS) give you Business Intelligence (BI) in the
form of Key Performance Indicators (KPIs) on your trading partners and your own operation. Most often, the information subject to these analyses are grouped into either financial
or performance-toplan categories. You
employ these analytics to make decisions
on future situations
based on historical
performance.
But the imperative
falls to you to determine the value of
good performance
and implement a
resulting decision
or reward structure.
You must make the
connection between the historical summary and the rule to influence behavior. The system
accumulates, homogenizes, and summarizes information in a way that you never could, but
until now there has been a gap in the process workflow -- connecting the KPIs to future decisions. Embedded Analytics (EA) makes this connection.
How You Work Today
Volume and duration requirements, performance-to-plan, comparative performance information, and historical cost information are readily available in today’s TMS. You can easily measure your carriers -- their on-time performance, acceptance/rejection percentage, billing accuracy, unanticipated accessorials, and others. You can examine historical costs, determine
trends, and posit target rates by lane. You can also determine the average time required to
load and unload at a facility, or to transit between your facility and repetitive destinations (or
pick-up points). You can examine workflow standards and adjust for trading partner performance or seasonal vagaries.
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The metrics the TMS delivers to you are simple and easy-to-understand and you have almost
infinite flexibility in selecting and summarizing the data. But, it is time-consuming for you
to implement these KPIs into your day-to-day operational decision-making. Employing this
information is largely a manual process. You must examine the KPI, make a decision on what
to influence, decide how to effect that influence, and implement your decision in the form of
a rule that the system employs when it makes future decisions of the appropriate type.
BI is delivered to you. To get your
metrics, you print a report, or view a
dashboard, with pre-defined metrics
reported for a given category. When
you have analyzed the results, you
return to your TMS and set rules to
make decisions and to reward good
performance and discourage bad
(making operational changes that
influence behavior in the future).
This process is separate from, and
asynchronous with, your day-to-day
transactional management of transportation operations.
In addition, most KPIs are backwardfacing and captured at a specific
point-in-time. Normally, BI is reported
monthly. This implies that toward the
end of the period and prior to the next
report, the information has become progressively more obsolete. The KPI’s value is only as
current as the last “snapshot,” since the metrics gathered over time change constantly. Each
rating or metric can improve or degrade over time, requiring you to constantly modify your
rules for decision or reward.
This is a flawed way-of-working. YOU need to get out of the middle of the process!
A Better Way-of-Working
You really want integral incorporation of this BI into your day-to-day operational business
process. Why not embed the BI that you already collect into the operational processes of
your TMS and employ it directly as decisions are required?
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Embedded Analytics takes the historical BI that is a byproduct of past shipments and examines it as operational decisions are being made. It automatically applies the most current
and relevant KPI to the specific operational decision being made. Because so many of these
decisions are impacted by constantly-changing performance metrics in multiple categories,
your TMS must become smart enough to make routine, but high-volume and current-datadependent, decisions for you.
It works by building a bridge between the analytics output and the rules. A macro extracts
the KPI data, transforms it (if necessary), and inserts the result into the appropriate rule for
operational decision-making.
Two common types of operational
decisions that can be affected involve
targets (either ‘not less than’ or ‘not
greater than’) and rankings (priority or
sequence). For example, for the TMS to
automatically impact a target, such as a
transit time within a lane, the KPI could
summarize shipment history within that
lane and determine both the average and
minimum transit times. These can then
be inserted into the route guide for that
lane, to be employed when future shipments are scheduled within that lane.
The key benefit of “closing-the-loop”
between the analytics and the application of business rules is timeliness of
information. The most current information -- either transactional or summary -is always employed implying that better
decisions are being made from better
information.
Embedded Analytics Examples of Use
There are a legion of examples of employing Embedded Analytics for both creating/implementing targets and adjusting rankings. Consider all the ways that Embedded Analytics can
affect your carrier relationships. You want to give preferential treatment to your best carrier
trading partners -- the ones who make it easier to do business with by delivering on time,
accepting all the loads you offer them on a timely basis, and billing correctly without
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unexpected fees. You want to maintain the best possible carrier relationships to enjoy the
best possible service.
Embedded Analytics allows you to automatically:
• reward your best performing carriers with quicker payment without having to
hand-analyze each transaction and individually decide carrier payment frequency
• c hange your route guide parameters to increase the number of loads offered to a carrier based on historical availability, superior acceptance, and on-time performance
• allocate
premium appointment slots to carriers that habitually demonstrate the best
on-time performance -- and make those slots unavailable to the poor performers
The operative word is automatically. Too many of these decisions are impacted by constantly-changing performance metrics in multiple categories. Your TMS can make these routine,
but high-volume and current-data-dependent, decisions for you. For example, EA can:
• Reward with quick payment the carriers that accept loads fully and quickly and perform on-time. By setting rules on performance levels that equate to payment speed, EA can automatically determine an individual carrier’s payment dwell time -- and change
that target time immediately as performance changes.
• Increase the number of loads offered to carriers based on historical availability,
superior acceptance percentage, and on-time performance (and within the limits of
your volume incentive agreements). Again, by setting ranking rules that affect the
allocation decision, employing metrics other than lowest rate, your TMS can determine
the best, most effective carrier to offer a load to, and do so without asking for your
intervention.
• A
llocate premium appointment slots to the carriers that habitually demonstrated the
best on-time performance so that your facilities operate smoothly in peak periods.
You want to show those peak periods as “unavailable” to carriers that have traditionally been late and caused schedule disruptions. By setting ranking rules that establish
minimum per formance standards for peak times, EA can automatically determine at
appointment request time whether a carrier’s historical on-time performance percentage qualifies for the time slot. The self-service appointment scheduling system can
mask peak appointment slots from poor performers, even if those slots are open. Each
carrier will see availability based on its historical on-time performance rank.
There are other significant areas where EA can be applied in addition to the carrier relationship. Rating is a key area because rates are volatile and only updating them periodically
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(annually or semi-annually as contracts change) implies that you may be losing money on a
day-to-day basis.
You can employ EA to develop target rates for quotes, or a “book-it-now” price. EA uses
historical information to calculate a rate by lane, compare this target rate to the existing rate,
and update target rates on a selective basis. Employing rules, EA can automatically keep this
rate up-to-date. For spot rate quotes, the “book-it-now” rate provides an eBay-like function
for a carrier to accept the load immediately.
The target rate becomes a target price for bidding. It can be a reference point to gauge when
the contracted rate is out-of-tolerance and should be reviewed prior to tender. The target rate
also provides a benchmark for analyzing rate trends and negotiating contracts. Particularly in
an environment where spot rates are negotiable, this results in a lower freight bill for you.
In addition to impacting the financial attributes, EA can be employed to impact operational
attributes as well. Determining the target duration for events and processes is an excellent
application for EA.
For example, EA can employ historical load/unload times and transit times to establish
targets to validate appointment duration and current transit time between appointments.
Automatically comparing historical load and unload times to current appointment duration
can flag those with insufficient duration. Automatically comparing transit time in a lane can
determine if sufficient time has been allowed between estimated facility departure time and
delivery appointment time and highlight those that are inadequate.
Other uses of EA include automatically monitoring and adjusting routing and volume allocations for seasonal impacts, accumulating volumes and frequencies of loads in lanes to
establish repetitive shipment templates, and adjusting roles and permissions on workflows
based on time and criticality.
Conclusion
In these examples, Embedded Analytics embeds Business Intelligence directly into your TMS
day-to-day operations and processes. The most current rate and performance information is
acquired interactively and the process is synchronous. It takes you out of the middle.
You could never manually-analyze each transaction and make these decisions as the events
arise. There are too many transactions that require examining specific metrics, and then
implementing individual operational decisions.
This process must be event-driven and the Business Intelligence must be examined by
Embedded Analytics without human intervention. Further, you do not want to permanently
change your carrier, rate, route, and appointment constructs. These embedded analytic
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decisions are dynamic, made transactionally as events arise, and changed dynamically as
each KPI changes.
Your time duration and capacity requirements change as performance dictates. You neither
permanently reward a trading partner for historically good behavior nor permanently punish
a traditionally bad-performing partner. Decisions and rewards are based on a continuum -and you constantly encourage the best performance that you desire1.
The examples cited in this article are but a few of the many ways Embedded Analytics can be
employed to improve your Transportation Management performance and reduce costs.
Note: Embedded Analytics is not optimization. Complex decisions are not being made from multiple variables
across multiple transactions concurrently. Rather, simple operational decisions are made on a transaction-bytransaction basis from performance history.
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