Big Data Analysis techniques, Essays (university) of Data Analysis & Statistical Methods

the six Big data analysis techniques

Typology: Essays (university)

2018/2019

Uploaded on 12/07/2019

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TMS Data
TMS data is an extremely flexible and useful source of insight into any company’s transportation
activities. Depending on the tool the data is sourced from, reporting can usually be done ad-hoc,
encompassing multiple levels of transportation records (orders, shipments, loads, etc.) Unfortunately,
the level of investment can be too high for many companies to adopt. TMS implementations can incur
significant costs, both in human and financial capital. However, the investment allows for complete
ownership and control over your companies transportation data.
There are many ways to take advantage of well-maintained TMS data. It is an excellent data source for
creating unbiased key performance indicator (KPI) metrics. This is because the TMS is generally owned
and operated by the company itself and contains data on all carriers, transportation modes, and
business units. Many times relying on carriers to provide on-time performance metrics on themselves
results in well overstated figures. Only data from your own planning tool can objectively capture and
standardize performance metrics across your transportation network. Additionally, TMS data is usually a
requirement anytime a network analysis or restructuring is considered. Unlike other data types, TMS
data is the most likely to contain planning characteristics and item/order level characteristics. These
data elements are critical in building modelling scenarios to evaluate the impacts of any network
change.
3PL / Carrier Data
Carrier sourced data comes in a very wide variety of formats. In many cases this data is sourced via an
intermediary (carrier rep, broker, etc.), but there are also a significant amount of carriers that have
online portals that allow for limited TMS like data extraction. 3PL’s often include limited levels of TMS
access and data availability. If your company does business with several carriers, then standardizing
each carrier’s data format will likely be required to drive any value. Additionally, companies are exposed
anytime they rely on a transportation partner as a data source in several alarming ways. First, the data
attained may be limited in completeness and/or relatability to internally held company data. Second,
the data has the potential to be inaccurate or presented in a way that positively reflects carrier
performance, while hiding service failures. And finally, in the event a carrier relationship is strained or
severed, there is no guarantee of retaining access to this data source.
As mentioned above, the ability to capitalize on carrier-provided data often hinges on the level of
completeness and depth. Carrier data containing complete lane, cost, and shipment or package
characteristics is invaluable during sourcing engagements. By aggregating and standardizing data from
all carrier partners, companies can be successful in structuring RFPs that target key impact areas. In the
absence of a TMS or execution platform, carrier data can also be used internally to better understand
things like inbound vendor management and carrier compliance, service level and mode utilization, and
average shipment characteristics.
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TMS Data

TMS data is an extremely flexible and useful source of insight into any company’s transportation activities. Depending on the tool the data is sourced from, reporting can usually be done ad-hoc, encompassing multiple levels of transportation records (orders, shipments, loads, etc.) Unfortunately, the level of investment can be too high for many companies to adopt. TMS implementations can incur significant costs, both in human and financial capital. However, the investment allows for complete ownership and control over your companies transportation data. There are many ways to take advantage of well-maintained TMS data. It is an excellent data source for creating unbiased key performance indicator (KPI) metrics. This is because the TMS is generally owned and operated by the company itself and contains data on all carriers, transportation modes, and business units. Many times relying on carriers to provide on-time performance metrics on themselves results in well overstated figures. Only data from your own planning tool can objectively capture and standardize performance metrics across your transportation network. Additionally, TMS data is usually a requirement anytime a network analysis or restructuring is considered. Unlike other data types, TMS data is the most likely to contain planning characteristics and item/order level characteristics. These data elements are critical in building modelling scenarios to evaluate the impacts of any network change.

3PL / Carrier Data

Carrier sourced data comes in a very wide variety of formats. In many cases this data is sourced via an intermediary (carrier rep, broker, etc.), but there are also a significant amount of carriers that have online portals that allow for limited TMS like data extraction. 3PL’s often include limited levels of TMS access and data availability. If your company does business with several carriers, then standardizing each carrier’s data format will likely be required to drive any value. Additionally, companies are exposed anytime they rely on a transportation partner as a data source in several alarming ways. First, the data attained may be limited in completeness and/or relatability to internally held company data. Second, the data has the potential to be inaccurate or presented in a way that positively reflects carrier performance, while hiding service failures. And finally, in the event a carrier relationship is strained or severed, there is no guarantee of retaining access to this data source. As mentioned above, the ability to capitalize on carrier-provided data often hinges on the level of completeness and depth. Carrier data containing complete lane, cost, and shipment or package characteristics is invaluable during sourcing engagements. By aggregating and standardizing data from all carrier partners, companies can be successful in structuring RFPs that target key impact areas. In the absence of a TMS or execution platform, carrier data can also be used internally to better understand things like inbound vendor management and carrier compliance, service level and mode utilization, and average shipment characteristics.

Invoice Data

Invoice Data also ranges widely in format types and level of detail. Parcel carriers, such as UPS and FedEx, generally provide a tremendous amount of detail in their invoice data making it the preferred data for parcel focused engagements. Other types of carriers on the other hand may only provide the bare minimum data detail in order to process payment. In cases where your company or your carriers do not engage in automated invoice data feeds (EDI, XML, FTP, etc.) the data may be sourced from paper invoices keyed into an accounts payable system. This tends to be problematic as it results in non- standardized formats and human errors such as miss-keys and omitted data fields. There are upfront costs in establishing this automated feed but in many cases carriers are happy to work with companies to mitigate these costs as there are benefits on the carrier side as well. Automated invoicing reduces the amount of administrative hours required on the carrier end as well as expediting their receivables. One major downside of invoice data is the time delay between execution and billing. Depending on the invoice schedule of the carrier or partner, invoice data may only be available several days or weeks after a shipment is executed. Invoice data is primarily used for audit purposes. Auditing of parcel and freight bills adds value by ensuring the accurate rating of shipments and correct application of accessorial and fuel surcharges. Invoice data can be directly compared to TMS data to identify cost variances and produce exception reports. If no TMS data is available, companies often contract with audit service providers that can simulate carrier contracts and execute audit and pay activities with a basic invoice data feed.