A Manufacturing Logical Data Model helps companies see the big picture.
by Steve Hoberman
Automated manufacturing processes are fascinating: Each machine must function
close to perfection independently yet work seamlessly with the other machines.
The manufacturing process works because the engineers understand the big
picture of how everything fits together.
This need to know the intricacies of a system is not limited to the production
process. The operational applications that run the production machinery and the
applications that automate other organizational processes such as accounting,
supply chain and logistics must also seamlessly work together and share
information as needed.
An organization's complexities must be well understood, captured and
communicated in the form of an enterprise data model (EDM). A data model uses
symbols and text to help developers and analysts better understand a set of
data elements and their business rules. An EDM is a subject-oriented and
integrated data model describing all of the data produced and consumed across
an entire organization.
"Subject-oriented" means that the concepts on a data model fit together as the
CEO sees the company, as opposed to how individual functional or department
heads see the company. There is one "Customer" term and definition, one "Order"
term and definition, etc.
Integration goes hand in hand with subject orientation and implies a single
view of the data along with a mapping back to the chaotic real world. For
example, if Customer Last Name lives in 10 applications within an organization,
the integrated EDM would show Customer Last Name only once and would capture
the mapping back to these 10 applications.
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Companies can save substantial amounts of time and money with a detailed and
well-proven industry logical data model.
EDMs help organizations integrate their data with the goals of improving
enterprise analytics, strategic planning and knowledge sharing. Because
resource and skill challenges can hinder the creation and maintenance of an
EDM, many organizations choose to purchase starter industry data models instead
of trying to reinvent the wheel.
An industry data model is a pre-built data model that captures how an
organization in a particular industry works or should work. Teradata offers
eight industry data models called industry logical data models, one of which is
the Manufacturing Logical Data Model (MLDM).
The details are in the data
The Teradata MLDM provides the big picture for a manufacturer and is, in
essence, a living and breathing view of the manufacturing business. It contains
83 broad subject areas, such as Inventory, Invoice and Item. The current
version of the MLDM is extremely robust, containing 1,649 entities, 6,092
attributes and 2,165 relationships. These numbers—and model features—are
continuously updated through new releases.
As Teradata Professional Services members work directly with clients in the
manufacturing field, they gather feedback for model changes and enhancements.
The Teradata Product Manager captures these new requirements so they can be
considered for addition in the next MLDM release. Also, market/industry trends
and innovations, such as the use of radio frequency identification (RFID), are
evaluated to ensure that additions to a new release provide increased value.
Each release of the MLDM, therefore, provides manufacturing clients with new
The MLDM has a number of important characteristics:
Operational. How a manufacturing company works instead of how
a manufacturing company typically does reporting is detailed in the MLDM. In
other words, the vast majority of the structures in the MLDM capture the data
elements and business rules that govern the day-to-day operations of the
Logical. The MLDM is completely independent of technology, so
its business concepts are not tainted by a particular type of software,
hardware or network.
Extensible. Most manufacturers use the MLDM as a foundation,
adding and removing structures and enriching the provided definitions to make
the data model more meaningful and distinct to the particular organization.
Abstract. A fair amount of abstraction is contained in the
MLDM. Abstract refers to combining like things together under generic terms
such as Event and Party to facilitate integration and gracefully handle future
Global. The structures and terms on the MLDM are designed for
Standard. Best practice naming standards, including the use of
class words, are followed in the identification of the data elements. A class
word is the last part of a data element name that represents the high-level
category to which the data element belongs, such as "code" and "amount."
Digestible. The MLDM is sectioned into subject areas. Subjects
are neatly captured in separate views, and the use of color distinguishing each
subject area makes it easier to digest the larger models.
To illustrate one use of the MLDM, let's examine the concept of Plan. Plan is a
critical concept to any manufacturing company, as it is a key indicator to
product demand and affects every part of the production process, from the
purchase of raw materials to the transportation of finished goods. Assume one
manufacturing company has three different definitions of Plan:
Night shift plant manager: A Plan is a good guess as to how
much inventory will sell within a time frame, such as a month or quarter. It
helps determine, among other things, the quantity of raw materials needed to
produce the finished products.
Carrier liaison in the transportation office: A Plan is a set
of point-to-point mappings that capture the route each carrier must travel to
deliver the finished goods to the warehouses, distribution centers or
Human resources department manager: A Plan is a career path
for each employee in the organization. In addition to identifying roles, this
Plan includes training and other forms of professional development.
A conceptual schema or conceptual data model (CDM) is a map of concepts and
their relationships that describes the semantics of an organization and
represents a series of assertions about its nature. The MLDM comes with a CDM
for the manufacturing industry that contains about 50 key concepts and their
relationships. It was built by including at least one major entity from each
subject area and then generalizing the rules among these remaining entities.
For example, more than 15 entities, including Invoice, Invoice Adjustment and
Invoice Type, are represented by just the single Invoice entity on the
Manufacturing CDM. The figure at right contains a subset of the
One of the entities on this Manufacturing CDM is Forecast Plan. The first
sentence in the three-paragraph definition of Forecast Plan provided in the
MLDM is: "This entity defines a collection of predictions and estimations by
particular timeframe, of product demand/sales/purchases/
production/etc. at some
designated level of aggregation." This definition will most likely satisfy the
night shift plant manager's definition of Plan.
Other subject areas in the Manufacturing CDM will accommodate the other views.
The Forecasts and Model Scores Subject Area carries the following sentence in a
two-paragraph definition of Plan: "This entity identifies an intended course of
This entity can then be copied into the CDM, and the modeling technique of
subtyping can be used to fit each definition into this concept. For example,
the human resources department manager's definition of Plan was called Employee
Development Plan, a new entity on the Manufacturing CDM with a detailed
definition provided by this manager. The carrier liaison's definition of Plan
was called Transportation Plan, with a detailed definition provided by the
This is an example of obtaining the big picture, the system's intricacies.
Eventually, these definitions must also be captured at a data element level. To
provide a taste of what is involved in obtaining this detailed information, one
of the Plan data elements from the source systems was mapped into the
corresponding MLDM data element. (See table.)
Note that this mapping is overly simplified, as usually complex transformation
rules as well as other types of metadata need to be reconciled, such as format,
granularity and nullability. Also note that the model needed to be expanded to
include the concept of Employee Development Plan, which was not on the original
MLDM. This illustrates the ease with which the model can be customized for a
Many integration battles are quickly defused using the MLDM, because instead of
win/lose definition debates among business areas, it becomes a mapping exercise
where both parties agree on a single, external, unbiased view.
Companies can save substantial amounts of time and money with a detailed and
well-proven MLDM. The generic data model serves as a foundation for companies'
EDMs and can be easily extended as the businesses grow. With an MLDM,
manufacturers are given a common understanding of business terms as well as a
big picture view of how each department, though independent, fits seamlessly
with all of the others.
Steve Hoberman has worked as a business intelligence (BI) and data management
practitioner and trainer since 1990. He is the inventor of the Data Model
Scorecard and author of two books, including "Data Modeling Made Simple."
Photo illustration by Jeff Grunewald
Teradata Magazine-September 2008