Proactive Supply Chain Performance Management with Predictive Analytics
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Course: | BUS606: Operations and Supply Chain Management |
Book: | Proactive Supply Chain Performance Management with Predictive Analytics |
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Date: | Saturday, September 7, 2024, 7:46 PM |
Description
Read this article. A predictive performance management model is introduced to manage complex business network collaborations and minimize uncertainty. Pay attention to the innovative performance management systems characteristics. What other attributes would you add to the list?
Abstract
Today's business climate requires supply chains to be proactive rather than reactive, which demands a new approach that incorporates data mining predictive analytics. This paper introduces a predictive supply chain performance management model which combines process modelling, performance measurement, data mining models, and web portal technologies into a unique model. It presents the supply chain modelling approach based on the specialized metamodel which allows modelling of any supply chain configuration and at different level of details. The paper also presents the supply chain semantic business intelligence (BI) model which encapsulates data sources and business rules and includes the data warehouse model with specific supply chain dimensions, measures, and KPIs (key performance indicators). Next, the paper describes two generic approaches for designing the KPI predictive data mining models based on the BI semantic model. KPI predictive models were trained and tested with a real-world data set. Finally, a specialized analytical web portal which offers collaborative performance monitoring and decision making is presented. The results show that these models give very accurate KPI projections and provide valuable insights into newly emerging trends, opportunities, and problems. This should lead to more intelligent, predictive, and responsive supply chains capable of adapting to future business environment.
Source: Marco Listanti, https://www.hindawi.com/journals/tswj/2014/528917/ This work is licensed under a Creative Commons Attribution 3.0 License.
Introduction
Today, supply chains are very complex business
networks that need to be managed collaboratively and optimized globally.
Additionally, global business landscape is constantly and rapidly
changing. Uncertainty, growing competition, shorter cycle times, more
demanding customers, and pressure to cut costs are just a few
characteristics of the 21st century business environment. It has become
critical to measure, track, and manage the performance of supply chain
processes. Performance management relates to application of processes,
methods, metrics, and technologies in order to create a consistent
relationship between supply chain strategy, planning, implementation,
and controlling.
Supply chain requires that member companies have
the means to assess the performance of the overall supply chain to meet
the requirements of the end customer. In addition, it is necessary to
be able to assess the relative contribution of individual member
companies within the supply chain. This requires a performance
measurement system that can not only operate at several different levels
but can also link or integrate the efforts of these different levels to
meet the objectives of the supply chain. In order to accomplish
this requirement, the performance measurement process will need to
provide methods and tools for measuring, monitoring, and managing supply
chain processes. Supply chain management (SCM) has received a
remarkable attention from both academia and industry since the last
decade; however, there is still lack of integration between SCM systems
and performance management systems. The huge majority of performance
measurement models and frameworks have focused on single organizations
or cover specific type of performance such as financial. There are
several performance measurement approaches specifically designed for the
supply chain management domain. Companies have to measure
performance at strategic, tactical, and operational levels with metrics
dealing with sourcing, making, delivering, and customer services.
When
companies use standardized metrics they can join benchmarking databases
and use benchmarking services to compare with best in class companies
and perform gap analysis. This analysis identifies weak points in the
supply chain that require some improvement through process redesign or
reengineering. Furthermore, standardized metrics facilitate
collaboration and integration within the supply chain and with 3rd party
logistic providers and outsourcing companies. Such collaboration models
are based on a clear definition of what is expected from the partners
and service providers in terms of process performance. Supply chain
collaboration improves competitive advantage and enables supply chain
partners to achieve synergies and create superior services.
In
order to achieve required supply chain agility and adaptivity, it is
necessary to use intelligent technologies and tools which enable
monitoring and evaluation of supply chain performance. To be
competitive, companies have to utilize business intelligence (BI)
technologies and tools in order to better manage their businesses and
anticipate the future. Combination of business intelligence and
performance management systems can improve supply chain efficiency and
accountability and reduce costs with optimized decision-making process
based on monitoring of the key performance indicators. In addition,
these systems should enable more predictable performance management by
providing actionable information to the right decision makers. The
resulting increased demand for business intelligence means that
companies should focus on the goal of providing all stakeholders with
the right information at the right time with the right tools. Achieving
this objective requires the use of BI solutions and applications for
tracking, analyzing, modelling, forecasting, and delivering information
in support of performance management and decision-making processes.
Performance
management (PM) complements BI and links people, strategies, processes,
and technology. The PM system can be a platform for the improvement of
supply chain operations. It usually provides information about what
happened, why something happened, and appropriate courses of actions.
The main goal of supply chain PM systems is business process
optimization through monitoring and analysis of key performance
indicators (KPIs). These performance measures enable supply chain
companies to align processes and activities with strategic objectives.
KPIs
are often used in BI systems to measure the progress of various metrics
against business goals. They have become very popular for BI analysis
because they provide a quick and visual insight into measurable
objectives. KPIs are customizable business metrics that present an
organization's status and trends toward achieving predefined goals in
clear and user friendly format. After a supply chain or member company
defines its strategy and objectives, KPIs can be defined to measure its
progress toward those objectives. KPIs are becoming essential elements
of supply chain performance management software, balanced scorecards,
and analytical dashboards.
Although more and more companies use
KPIs for measuring business performance, these key performance
indicators are typically internal, financial, and functional. Financial
accounting measures are certainly important in assessing whether or not
operational changes are improving the financial health of an enterprise
but are insufficient to measure supply chain performance for the
following reasons.
(i) The measures tend to be historically oriented and not focused on providing a forward-looking perspective.
(ii) The measures do not relate to important strategic, nonfinancial performance.
(iii) The measures are not directly tied to operational
effectiveness and efficiency.
(iv) Most performance measurement systems
are functionally focused.
Supply chain performance measurement requires specific metrics which are global, process based, multitiered, and comprehensive.
The
problem with most of existing PM systems is twofold. The first one is
related to data. Supply chains usually hold a vast amount of distributed
and heterogeneous data. Integration of this isolated and often
incompatible data is a big challenge. To be able to make better
decisions based on facts, organizations need to get this factual
information typically from several information systems, integrate this
data in a useful way, and present users with reports and analysis that
will help them to understand the past and the present organizational
performance.
Secondly, KPIs are traditionally retrospective, for
example, showing last month's stock level compared to the stock target.
However, with insights made possible through data mining, organizations
can build predictive KPIs that forecast future performance against
targets, giving the business an opportunity to detect and resolve
potential problems proactively.
The next step in promoting supply
chain agility and operational efficiency is to make the leap from
retrospective analysis of historical KPIs to proactive actions based on
predictive analysis of supply chain performance data and to embed
intelligent, fact-based decision-making into business processes. The key
to accomplishing this is to use powerful data mining algorithms to
analyze data sets, compare new data to historical facts and behaviors,
identify classifications and relationships between business entities and
attributes, and deliver accurate predictive insights to all systems and
users who make business decisions.
The remainder of this
paper is organized as follows. It starts with literature review related
to supply chain performance management and predictive analytics. Then, a
unified approach to supply chain performance management which
integrates supply chain process model, online analytical processing
(OLAP), KPIs, data mining predictive analysis, and web portals is
presented.
Background Research
Performance management represents a
cohesive element that unites different business improvement initiatives,
directs strategy formulation, and plays the key role in strategy
implementation and monitoring. PM is of great importance for managing
supply chains. Timely and accurate evaluation of the entire supply chain
and individual companies is prerequisite for successful functioning.
Performance management should be a part of any proper supply chain
strategy, planning, and reporting process.
There has been lot of
research efforts during the last decade that deal with various aspects
of supply chain performance management. Shepherd and Gunter provide
taxonomy of supply chain performance measurement systems and metrics
with critical review of the contemporary literature. The study shows
that despite substantial advances in the literature in the last decade,
there are still important topics related to supply chain performance
management that did not receive adequate attention, such as process
modeling, data integration, software support, and forecasting. Estampe
et al. provide analysis of various supply chain performance models by
stressing their specific characteristics and applicability in different
contexts, so that decision-makers can evaluate and apply models and
metrics that best suit their needs. Arzu Akyuz and Erman Erkan also
provide a critical literature review on supply chain performance
measurement. The results show that performance measurement in the new
era is still an open area of research. This particularly refers to
framework development, collaboration, agility, flexibility, and IT
support systems. Gopal and Thakkar give a comprehensive review of supply
chain performance measurement systems. The analysis shows that, in
spite of considerable evidence from the literature, there is a large
scope for research to address critical issues in supply chain
performance measurement, including metrics, benchmarking, integration,
business intelligence, and collaborative decision making.
The
goal of supply chain performance management is business process
optimization through monitoring and analysis of key performance
indicators. By measuring and monitoring metrics against predefined goals
companies can provide added value to large volumes of data generated
over time. This type of analysis allows companies to track various
metrics at different organization levels and to take timely actions.
This
has elevated the importance of key performance indicators and their
ability to measure, predict, and manage the business health of a supply
chain in real (or near real) time. KPIs have morphed from static siloed
measures to dynamic real-time enterprise metrics. Key performance
indicators are normally backward-looking because they are based on
historical information and often do not help in forecasting future
events or performance. Predictive metrics would make it possible to
predict future problems in supply chain operations and to enable
proactive evaluation and improvement actions in advance. Statistical
modelling and data mining under the guise of predictive analytics have
become critical building blocks in setting the new KPI standard for
leading indicators.
However, most of the existing
performance measurement systems are mostly based on financial indicators
(i.e., costs). They are also internally focused, incompatible, and
historical. In today's business environment this performance measurement
approach is no longer adequate. New business climate requires novel and
innovative performance management systems which have the following
characteristics:
(ii) comprehensive (takes into account relevant variables),
(iii) use also non-financial KPIs,
(iv) adaptable to specific supply chain configuration and member companies,
(v) optimal number of KPIs,
(vi) simple and easy to use,
(vii) timely and actual,
(viii) accurate and consistent,
(ix) foster improvement, not just monitoring,
(x) represent information at different supply chain levels,
(xi) provide analytical tools that offer multidimensional reporting,
(xii) enable a proactive management, rather than reactive,
(xiii) provide insights into newly emerging trends, opportunities and problems,
(xiv) deliver information to right people at any time and on any device.
In response to these
challenges for measuring supply chain performance, a variety of
measurement approaches have been developed, including the following: the
balanced scorecard, the supply chain council's SCOR model, the
logistics scoreboard, activity-based costing (ABC), and economic value
analysis (EVA). Lambert and Pohlen introduced a framework for
developing metrics that measure the performance of key supply chain
processes, identify how each firm affects overall supply chain
performance, and can be translated into shareholder value. Cai et
al. propose a framework for analyzing supply chain KPIs accomplishment,
so that management strategy can be attuned by interpreting the analysis
results. This framework aligns performance at each link
(supplier-customer pair) within the supply chain. Gunasekaran et al.
developed a framework for supply chain performance measures and metrics
considering the four major supply chain processes: plan, source, make,
and deliver. Metrics were classified at strategic, tactical, and
operational levels in order to clarify management authority and
responsibility for performance.
In an attempt to overcome some
limitations of traditional PM systems, many companies have initiated
balanced scorecard (BSC) projects. Based on the methodology of Robert
Kaplan and David Norton, these companies created a balanced set of
metrics representing financials, customers, internal business processes,
and innovation. The goal was to enable better decision making by
providing managers with a broader perspective of both tangible and
intangible assets. However, the supply chain has specific processes and
metrics which require special types of balanced scorecards. Bhagwat and
Sharma proposed a balanced scorecard for supply chain management that
measures and evaluates day-to-day business operations from the four BSC
perspectives and with specific SCM metrics.
Only a few
leading-edge companies are currently using true extended PM systems
(either in-house developed or implemented PM software applications) that
not only measure the performance of their enterprise but also measure
that of their supply chain wide activities. Most companies are still in
the internal or integrated stage of the maturity model (as shown in
Figure 1) where they focus on the performance of their own enterprise
and measure their supply chain performance with financially oriented
metrics.
Figure 1 Supply chain performance measurement maturity model.
![](https://learn.saylor.org/pluginfile.php/5619163/mod_book/chapter/50114/528917.fig.001.png?time=1716315548544)
One
of the prerequisites for effective supply chain performance measurement
is the initiative geared toward standardization of supply chain
processes and metrics. Standardized models facilitate application
integration and collaboration, enable benchmarking for performance
comparison, and provide best practices for process improvement and
gaining a competitive advantage. By standardizing supply chain processes
and metrics for managing such processes, companies are able to not only
compare their results against others, but to also gain visibility of
operations over a supply chain that may cross corporate borders.
Partners in a supply chain can communicate more unambiguously and can
collaboratively measure, manage, and control their processes.
SCOR
(supply chain operation reference) model represents a universal
approach to supply chain management that can be applied in diverse
business domains. SCOR combines business process engineering,
benchmarking, and best practices into a single framework. This
standardization movement facilitates the deliverance of business content
for the supply chain KPI and predictive analysis model. Kocaoğlu et al.
propose a SCOR based approach for measuring supply chain performance. It uses the analytic hierarchy process (AHP) and technique for
order preference by similarity to ideal solution (TOPSIS) together for
the linking of strategic objectives to operations.
Although the
SCOR model provides a layered metric system and thereby a business
context for the KPI system, it is only the first step toward the
pervasive supply chain performance measurement. In order to implement
such a complex PM system as found in supply chains, member companies
need to extract, transform, and load all the relevant data into a unique
and integrated data source.
Supply chain integration is critical
to both operational and business performance. In order to achieve the
full effect of supply chain integration, companies need to use
collaborative and intelligent web information systems to actively manage
process uncertainty. Most of the existing information systems that
support supply chain management are repositories for large amounts of
transactional data. These systems are said to be data rich but
information poor. The tremendous amount of data that is collected and
stored in large, distributed database systems has far exceeded the human
ability of comprehension without analytic tools. Analysts estimate that
80% of the data in a transactional database that supports supply chain
management is irrelevant to decision making and that data aggregations
and other analyses are needed to transform the other 20% into useful
information.
As a result of the huge amount of data
generated within supply chains, new tools and methods should be
developed which are capable of storing, managing, and analyzing the
data, as well as of monitoring global supply chain performance. Chae and
Olson propose an analytical framework for supply chain management,
which is composed of three IT-enabled capabilities: data management,
analytics, and performance management.
Increasingly,
transactional supply chain data is processed and stored in enterprise
resource planning systems, and complementary data warehouses are
developed to support performance management and decision-making
processes. Some organizations have developed data warehouses and
integrated data-mining methods that complement their supply chain
management operations.
Investment in business
intelligence software is necessary if the organizations want to manage
their supply chain more effectively. The studies show that main drivers
for BI initiatives include complexity of operations, huge amounts of
data, requirements for cost reduction, increased revenue, and efficiency
improvement, as well as high exposure to risks. Data warehousing
and online analytical processing technologies combined with tools for
data mining and knowledge discovery have allowed the creation of systems
to support organizational decision making.
Inventory
management is probably the key supply chain process, and inventory costs
represent a large portion of the total supply chain costs.
Incorporating predictive analytics in an inventory management process
can lead to many benefits such as cost reduction, higher customer
service level, optimal reordering policy, enhanced productivity, shorter
cash-to-cash cycle time, and ultimately increased profitability.
Different data mining techniques were used for solving supply chain and
inventory management related problems. Dhond et al. used neural-network
based techniques for inventory optimization in a medical distribution
network which resulted in 50% lower stock levels. Symeonidis et al.
applied data mining technology in combination with an autonomous agent
to forecast the price of the winning bid in a given order. When it
comes to inventory management, Stefanovic et al. used different data
mining models clustering retail stores based on sales patterns and
one/two weeks out-of-stock forecasting.
When data mining
predictive analysis capabilities are closely integrated into every stage
of the data life cycle, they incorporate intelligence into reporting,
data integration, OLAP analysis, and business performance monitoring.
This helps supply chains to increase business agility and creates a
tangible competitive advantage.
As supply chain performance
management systems mature, it is almost certain that predictive
analytics will be an integral part of future PM and BI solutions.
Predictive analytics leverage historical and current performance data in
order to make predictions on future performance. For example, there
could be a predictive analytical model that can make predictions about
supply chain management costs, perfect order fulfillment, return on
working capital, and so forth. The main difference between predictive
analytics and classic BI is that predictive models contain an additional
stage which utilizes certain data mining algorithms to predict future
performance outcomes. This information can be used as a valuable
resource for the decision-making process and refinement of strategic,
tactical, and operational supply chain plans. One recent survey showed
that 87 percent of respondents said that they think predictive analytics
is important to the budgeting and planning process, but only 17 percent
are employing technologies that include the capability. Other
studies show that the ROI of predictive mining applications is almost
five times greater than that of nonpredictive applications using
standard query, reporting, and analysis tools.
Although with
great practical potential, there have not been many research projects
related to predictive performance management. Seifert and Eschenbaecher
introduced a predictive performance measurement approach as a planning
tool for virtual organizations to anticipate the performance of a
planned virtual team. Derrouiche et al. proposed an integrated
framework for supply chain performance evaluation based on data mining
techniques. It enables the development of a predictive
collaborative performance evolution model and decision making which has
forward-looking collaborative capabilities. Maleki and Cruz-Machado
proposed a framework for developing data mining models based on Bayesian
networks which account uncertainty and mutual dependency among supply
chain performance measures.
Predictive KPI analytical models
can identify critical KPIs and their likely impact on other indicators,
both at the same level or higher-level KPIs. Besides typical predictive
data mining models, other models can be created in order to further
clarify prediction results, analyze key influencers, and perform
scenario analysis such as goal seeking and what-if analysis. Thus,
predictive PM systems will not only notify users about KPI risks, but
will also provide additional information necessary for taking corrective
actions.
On the other hand, most of the existing PM systems are
not business user-friendly which makes them less attractive for wider
implementations. Modern PM systems have to be web-based, interoperable,
modular, and customizable.
Based on the analysis of the existing
research results and also the methods and tools used in real-world
supply chain operations, two main conclusions can be
derived.
(i) Organizations show a strong interest in proactive supply chain performance management, but very few PM projects deal with predictive analytics.
(ii) Most of existing research efforts, methods, and
tools are targeted to specific supply chain PM aspects, without a
comprehensive approach which would provide organizations with concrete
models, methods, and tools for achieving business objectives.
What
is needed is a unified supply chain performance management system to
collect, integrate, and consolidate all relevant data and to use
business intelligence tools like data warehousing and data mining, to
discover hidden trends and patterns in large amounts of data, and
finally to deliver derived knowledge to business users via web portals.
In this context, a novel supply chain PM model and software solution
that fulfills most of the previously stated requirements are proposed.
In the subsequent sections, more information related to supply chain
OLAP modelling, KPI design, data mining KPI prediction model, and PM web
portal is presented.
Predictive Supply Chain Performance Management Model
Performance
management of complex business networks such as supply chains requires a
unified approach that comprises different management models,
technologies, and tools. This section introduces an integrated supply
chain PM model which incorporates the supply chain modelling method and
business intelligence technologies such as data warehousing and data
mining. It is based on the integrated supply chain intelligence model
for collaborative planning, analysis, and monitoring.
The main elements and the structure of the supply chain PM model are shown in Figure 2.
Figure 2 Predictive supply chain performance management model.
![](https://learn.saylor.org/pluginfile.php/5619163/mod_book/chapter/50115/528917.fig.002.png)
The
basis of the model is the supply chain modelling method which enables
modelling of supply chain processes, relationships, metrics, best
practices, and other relevant elements. Output of this stage is a supply
chain process model that serves as input for data warehouse design.
First,
based on the process model, data from various sources is extracted,
cleaned, and transformed in order to accommodate requirements for KPI
design, multidimensional analysis, and data mining models. The following
step is construction of OLAP cubes with proper dimensions and measures.
OLAP schema is the basis for design of supply chain KPIs which measure
the progress toward predefined goals. KPI typically provides a visual
representation of metrics over time, rather than just displaying the
numbers.
The next step which includes data mining is the key step
toward predictive performance management. Here, historical performance
(KPI) data is used for making predictions about future performance. This
information is then delivered to decision making via a special BI web
portal in the form of web reports, charts, scorecards, dashboards, or
notifications. Alternatively, prediction information can be used in
supply chain simulation models for analyzing different scenarios and
risks. Abukhousa et al. used simulation models as an analysis tool
for predicting the effects of changes to existing healthcare supply
chains and as a design tool to predict the performance of new systems
under varying sets of input parameters or conditions.
The
final step is taking appropriate actions to resolve problems and make
adjustments to strategy and plans. These actions can be made based on
more detailed reporting, data exploration, specific expert systems,
simulation, and data mining models. One such intelligent software
solution for integrated and interactive supply network design,
evaluation, and improvement is developed. It consists of three
modules designed for knowledge-based supply network modelling,
rule-based simulation executions, and intelligent assessment and
improvement.
In the next subsections, the main elements of the proposed supply chain predictive PM model will be described.
Supply Chain Process Model
The
starting point is the SCOR process model which provides a library of
the supply chain specific set of processes, relationships, metrics, and
best practices. The SCOR process model contains a standard name for each
process element, a notation for the process element, a standard
definition for the process element, performance attributes that are
associated with the process element, metrics that are associated with
the performance attributes, and best practices that are associated with
the process.
All process metrics are an aspect of the performance
attribute. Performance attributes for any given process are
characterized as either customer-facing (reliability, responsiveness,
and flexibility) or internal-facing (cost and assets) metrics.
Top
level metrics are the calculations by which an implementing company can
measure how successful they are in achieving their desired positioning
within the competitive market space. Lower level calculations (levels 2
and 3 metrics) are generally associated with a narrower subset of
processes. For example, delivery performance is calculated as the total
number of products delivered on time and in full, based on a committing
date. Additionally, even lower level metrics (diagnostics) are used to
diagnose variations in performance against plan. For example, a company
may wish to examine the correlation between the request date and
committing date.
Each process from the process model has its
related metrics, best practices, and inputs and outputs. All metrics
follow the same template which consists of the following
elements:
(i) name,
(ii) definition,
(iii) hierarchical metric structure,
(iv) qualitative relationship description,
(v) quantitative relationship (optional, if calculable),
(vi) calculation,
(vii) data
collection.
Based on the SCOR process model, we have created the
SCM metamodel (Figure 3), which enables the creation of any supply chain
configuration and is the basis for further modeling Metamodel is
normalized and contains all SCM elements such as processes, metrics,
best practices, inputs, and outputs. It also incorporates business logic
through relationships, cardinality, and constrains.
Figure 3 SCM metamodel.
![](https://learn.saylor.org/pluginfile.php/5619163/mod_book/chapter/50115/528917.fig.003.png)
Metamodel
is extended with additional entities to support supply network
modelling. That way, processes, metrics, and best practices can be
related to a specific node and tier in the supply network. With this
metamodel, processes at different levels can be modelled, thus providing
a more detailed view of supply chain processes and metrics. Metamodel
database contains standard SCOR metrics but also enables defining of
custom metrics, as well as metrics at lower levels (i.e., Level 4,
workflows, or Level 5, transactions).
The developed SCM metamodel
enables flexible modelling and creation of different supply chain
configurations (models). These models are the basis for the construction
of data warehouse (DW) metadata (measures, dimensions, hierarchies, and
KPIs).
DW and OLAP KPI Modeling
A user who wants to retrieve information directly from a data source, such as an ERP database, faces several significant challenges.
(i) The contents of such data sources are frequently very hard to understand, being designed with systems and developers instead of users in mind.
(ii) Information of interest to the user is typically distributed among multiple heterogeneous data sources.
(iii) Whereas many data sources are oriented toward holding large quantities of transaction level detail, frequently the queries that support business decision making involve summary and aggregated information.
(iv) Business rules are generally not encapsulated
in the data sources. Users are left to make their own interpretation of
the data.
In order to overcome these problems, we have
constructed the unified dimensional model (UDM). The role of a UDM
is to provide a bridge between the user and the data sources. A UDM is
constructed over one or more physical data sources. The user issues
queries against the UDM using a variety of client tools.
Construction
of the UDM as an additional layer over the data sources offers clearer
data model, isolation from the heterogeneous data platforms and formats,
and an improved performance for aggregated queries. UDM also allows
business rules to be embedded in the model. Another advantage of this
approach is that UDM does not require data warehouse or data mart. It is
possible to construct UDM directly on top of OLTP (on-line
transactional processing) systems and to combine OLTP and DW systems
within a single UDM.
In the UDM it is possible to define cubes,
measures, dimensions, hierarchies, and other OLAP elements, from the DW
schemas or directly from the relational database. This enables providing
the BI information to the business users even without previously built
DW, which can be very useful having in mind the fact that within the
supply chain there can be many nonintegrated data sources which require
time to connect, integrate, and design the data warehouse.
Flexibility
of UDM is also manifested in the fact that tables and fields can be
given names and descriptions that are understandable to the end-user and
hide unnecessary system fields. This metadata is further used
throughout the UDM, so all the measures, dimensions, and hierarchies
that are created based on these table fields will use these new names.
Definitions
of all UDM elements are stored as XML (eXtensible markup language)
files. Each data source, view, dimension, or cube definition is stored
in a separate XML file. For dimensions, these files contain data about
tables and fields which store dimension members. OLAP cube definition
files also contain information on how the preprocessed aggregates will
be managed. This metadata approach enables centralized management of the
dimensional model for the entire supply chain and provides an option
for model integration and metadata exchange.
Measures are one of
the basic UDM elements. Measures are the primary information that
business users require in order to make good decisions. Some of the
measures that can be used for the global supply chain analysis and
monitoring are as follows:
(i) reliability:
(1) perfect order fulfillment,
(ii) responsiveness:
(1) order fulfillment cycle time,
(iii) agility:
(2) upside supply chain adaptability,
(3) downside supply chain adaptability,
(4) overall value at risk,
(iv) cost:
(1) total cost to serve,
(v) asset management efficiency:
(2) return on supply chain fixed assets,
(3) return on working capital.
During the design, for each measure we need to define
the following properties:
(i) name of the measure,
(ii) what OLTP field or fields should be used to supply the data,
(iii) data type (money, integer, or decimal),
(iv) formula used to calculate the measure (if there is
one).
The next step is to cluster measures into measure groups.
The measure groups are an integral part of the UDM and the cube. Each
measure group in a cube corresponds to a table in the data source view.
This table is the measure group's source for its measure data.
Supply
chain process model (built using the SCM metamodel) can be used as the
basis for defining measures and measure groups because it provides
relationships between business processes and metrics, metrics
hierarchies, definitions, quantitative and qualitative descriptions, and
description of possible data sources that provide data for
calculations.
Companies often define key performance indicators,
which are important metrics used to measure the health of the business.
An OLAP KPI is a server-side calculation meant to define company's most
important metrics. These metrics, such as net profit, assets
utilization, or inventory turnover, are frequently used in dashboards or
other reporting tools for distribution at all levels throughout the
supply chain.
The UDM allows such KPIs to be defined, enabling a
much more understandable grouping and presentation of data. Key
performance indicator is a collection of calculations that are
associated with a measure group in a cube that is used to evaluate
business success. Typically, these calculations are a combination of
multidimensional expressions (MDX) or calculated members. KPIs also have
additional metadata that provides information about how client
applications should display the results of the KPI's calculations. The
use of OLAP-based KPIs allows client tools to present related measures
in a way that is much more readily understood by the user.
Table 1 lists common KPI elements and their definitions.
OLAP KPI structure.
Term | Definition |
Goal | An MDX numeric expression that returns the target value of the KPI. |
Value | An MDX numeric expression that returns the actual value of the KPI. |
Status | An MDX expression that represents the state of the KPI at a specified point in time. The status MDX expression should return a normalized value between −1 and 1. |
Trend | An MDX expression that evaluates the value of the KPI over time. The trend can be any time-based criterion that makes sense in a specific business context. |
Status indicator |
A visual element that provides a quick indication of the status for a KPI. The display of the element is determined by the value of the status MDX expression. |
Trend indicator | A visual element that provides a quick indication of the trend for a KPI. The display of the element is determined by the value of the trend MDX expression. |
Display folder | The folder in which the KPI will appear to the user when browsing the cube. |
Parent KPI | A reference to an existing KPI that uses the value of the child KPI as part of the KPI's computation. |
Current time member |
An MDX expression that returns the member that identifies the temporal context of the KPI. |
Weight | An MDX numeric expression that assigns a relative importance to a KPI. If the KPI is assigned to a parent KPI, the weight is used to proportionally adjust the results of the KPI value when calculating the value of the parent KPI. |
Figure 4 shows a part of the return on supply chain fixed assets KPI defined in the OLAP server.
Figure 4 DX KPI definition.
![](https://learn.saylor.org/pluginfile.php/5619163/mod_book/chapter/50115/528917.fig.004.png)
Besides
aforementioned benefits of the UDM model, it also serves as a basis for
data mining model design because it provides a consolidated,
integrated, aggregated, and preprocessed data store.
Data Mining Model for KPI Prediction
While proliferation of
reporting and multidimensional analytics has greatly benefited many
organizations of all sizes, the next step in promoting business agility
and operational efficiency is to make the leap from retrospective
analysis of historical data to proactive actions based on predictive
analysis of business data and to embed intelligent, fact-based decision
making into business processes. The key to accomplishing this is to use
powerful data mining algorithms to analyze large data sets, compare new
data to historical facts and behaviors, identify classifications and
relationships between business entities and attributes, and deliver
accurate predictive insights into all of the systems and users who make
business decisions.
Building a data mining model is a part of a
larger process that includes everything from defining the basic problem
the model will solve to deploying it into a working environment. A model
typically contains input columns, an identifying column, and a
predictable column. Data type for the columns can be defined in a mining
structure based on which algorithms process the data. Depending on the
case, a column can be the following:
(i) continuous column: this column contains numeric measurements typically the product cost, salary, account balance, shipping date, and invoice date having no upper bound.
(ii) discrete column: these are finite unrelated values such as product category, location, age, and telephone area codes. They do not need to be numeric in nature and typically do not have a fractional component.
(iii) discretized column: this is a continuous column converted to be discrete, for example, grouping salaries into predefined bands.
(iv) key: the column which uniquely identifies the row, similar to
the primary key.
Different models for a given business problem
could be used for analyzing various business scenarios, identifying the
analytical requirements, tuning the parameters, and evaluating the
results of the models to make a business decision.
Predictive
models can be used to forecast explicit values, based on patterns
determined from known results. For example, we can define the target
customer service level KPI and set the target to 95%. Then, based on the
historical data, a model can be built that predicts this KPI in the
future.
There is a variety of techniques developed to achieve
that goal - typically applying different models to the same data set and
then comparing their performance to choose the best one. For the KPI
prediction, different DM models and algorithm can be used depending on
the goal and business case. Here, the details about considering various
models and choosing the best one based on their predictive performance
(i.e., explaining the variability in question and producing stable
results across samples) are briefly explained.
(i) Classification algorithms (such as decision trees) predict one or more discrete variables, based on the other attributes in the dataset.
(ii) Regression algorithms predict one or more continuous variables, such as profit or loss, based on other attributes in the dataset.
(iii) Time series algorithms forecast the patterns based on the current set of continuous predictable attributes.
(iv) Association algorithms find correlations
between different attributes in a dataset. The most common application
of this kind of algorithm is for creating association rules, which can
be used in a market basket analysis or KPI analysis.
Choosing the
right algorithm to use for a specific business task can be challenging.
While it is possible to use different algorithms to perform the same
business task, each algorithm produces a different result, and some
algorithms can produce more than one type of result. For example,
decision trees algorithm can be used not only for predictions, but also
as a way to reduce the number of columns in a dataset, because the
decision tree can identify columns that do not affect the final mining
model. The type of algorithm depends on the type of prediction. For
example, if we are predicting a discrete attribute (i.e., out-of-stock)
we can use naïve Bayes, decision trees, or neural networks. If we are
predicting a continuous attribute (i.e., supply chain sales amount), a
time series algorithm can be used. If we are predicting a sequence
(i.e., analyzing the factors leading to delivery failure), a sequence
clustering can be used. Using more than one data mining model over the
same mining structure is a good practice, since the best model can be
selected for predictions. Lift charts can be useful tool to check the
accuracy of the data mining models once built on the input data.
In
contrast to standard KPIs that only report past or at best the present
performance, predictive KPI looks forward and use data mining to show
what the situation will be in the next month, quarter, or year. For
example, we can use customer data to predict a future demand and thus
better plan the production and inventory management processes. Or we can
predict disruption in delivery and proactively plan alternative
delivery modes.
This allows organization to react before certain
disruption happens. Predictive KPI can give the insight into emerging
trends or into potential opportunities or problems.
The advantage
of using the OLAP-based KPIs is that they are server based and can be
consumed by a variety of clients. This means that each client throughout
the supply chain will access a single version of the truth, thus making
coordination efforts much easier. Also, making a complex calculation on
the server can have performance benefits.
Building Prediction Data Mining Models
In this section, two approaches for building KPI prediction models within the UDM are introduced:
(i) using OLAP data mining dimensions,
(ii) using
prediction tables.
Data mining dimensions are results of
predictive calculations which are saved into the cube as new dimensions.
These dimensions can be browsed or even sliced and diced by results of
predictions just as with any other dimension. The special MDX predict
function can be used to perform predictions. This allows performing
prediction joins against data mining models from queries within the
cube. When calculating KPI elements such as value, target, or status, we
make use of the data mining dimension.
This procedure can be
used for various supply chain analysis tasks such as inventory
out-of-stock prediction, supplier lead time prediction, and forecasting
of customer demand or order fulfillment time. For these tasks, different
data mining algorithms such as decision trees, time series, or neural
networks can be used. Models can be evaluated and compared in terms of
accuracy and precision.
The alternative way for making KPI
predictions is using the prediction tables. Prediction tables are just
any other tables used for an OLAP cube. They can be a measure group or a
dimension, but typically they will be a measure group. In this approach
data mining predictions are performed within the ETL (extract,
transform, and load) process. ETL package pulls data from the data
source, performs a prediction task, and loads the results into a new
prediction table. This gives us greater flexibility because we can add a
new table to the data warehouse. It is also more flexible for
scheduling the training of the model and for maintaining the model. The
model can be defined outside the cube and does not need to be processed
along with the cube. So, the model can be replaced if we find the better
model without altering the cube.
Figure 5 shows an example of
the ETL data and control flows that perform data extraction,
integration, cleansing, and data mining prediction.
Figure 5 Data mining ETL package.
![](https://learn.saylor.org/pluginfile.php/5619163/mod_book/chapter/50116/528917.fig.005.png)
In
the first step ETL package pulls data from different supply chain
sources and passes it to a certain data processing component and then
into the prediction component and data mining query. This can call any
OLAP server or data mining model and return the results in the ETL
pipeline. Then, we can perform different operations: populate measures,
split predictions into good and poor predictions, or define any type of
filtering or modifications. In order to design such ETL packages we used
special ETL components such as a data mining model training destination
for training data mining models and a data mining query transformation
that can be used to perform predictive analysis on data as it is passed
through the data flow. The MDX expressions for building KPIs over
prediction tables are just the same as for any other KPIs.
Both
presented predictive modeling approaches can be used for making the KPI
predictions, so the designers have possibility chosen appropriate design
approach. The selection depends on the particular scenario. Generally,
data mining dimensions offer slightly better performance and slice and
dice capabilities, but data mining models must be within the same cube
which means it is not possible to use data mining models from another
cube or server. On the other hand, the approach with prediction tables
performs predictions within the ETL service, instead of the OLAP
service. This imposes some additional load on the server during ETL
package execution, whereas OLAP cubes can be preprocessed before
deployment. However, prediction tables offer more flexibility in terms
of scheduling and maintenance, and the models can be defined and
maintained outside the cube or replaced with better (more accurate)
models without altering the cube. Also, this approach is more suitable
for integration scenarios, where supply chain partners can have
different analytical systems.
Validation of Data Mining Prediction Models
Before
deploying and utilizing prediction models into production, they must be
validated. This is a very important step in the data mining process
because we need to know how well models perform against actual data. For
the validation of the proposed predictive models, a real-world data set
from the automotive company was used.
There is no single
all-inclusive method which can prove quality of the data and the model.
There are several approaches for evaluating the quality of a data mining
prediction model. We can use various statistical techniques or involve
supply chain domain experts to analyze the prediction results.
Furthermore, we can split existing data set into training and testing
sets in order to check the accuracy of the model. The training set is
used to create the mining model. The testing set is used to test model
accuracy.
These approaches are not mutually exclusive but can be
combined together during design and testing phases to refine models
through series of iterations. Various tools can be used for testing data
mining prediction model: lift charts, profit charts, scatter plots,
classification matrix, cross-validation, and so forth. Figure 6
illustrates sales quantity trends and predictions deviation for a single
product at three different geographic regions.
Figure 6 Forecasting data with deviations.
![](https://learn.saylor.org/pluginfile.php/5619163/mod_book/chapter/50116/528917.fig.006.png)
Validation
needs to include different measures which relates to accuracy,
reliability, and usefulness of the prediction models. Accuracy tells us
how well the model correlates the results with the attributes in the
data set. Reliability is also very important characteristic of the
prediction models which shows how effective the model is with different
data sets. This is especially significant in supply chains that include
different divisions and organizations with various data sets. If the
model produces similar types of predictions or kinds of patterns, it can
be considered reliable.
And finally, data mining models have to
be useful, meaning that they need to provide certain answers and to
support the decision-making process. For example, if percentage of
orders that are fulfilled on the customer's originally committed date is
decreasing, we need to know why. This is where the key influence
analysis comes into action.
End-User Analytics
Once we
define OLAP-based predictive KPIs, we can use different client
applications for browsing and for slicing and dicing based on various
criteria. For example, UDM model enables slicing data by different
dimensions (i.e., organization, geography, product, etc.) or dimension
hierarchies. Furthermore, data can be filtered by particular values. For
example, we can display prediction of the cash-to-cash cycle time KPI
for particular year and quarter, country, and organization.
Additionally,
predictive analysis can detect attributes that influence KPIs. Business
users can monitor trends and analyze key influencers in order to
identify those KPIs (attributes) that have a sustained effect or
significant positive or negative impact, for example, identifying
whether price discount on a certain product has long-term impact on
sales or only produces a short-term effect. Such actionable insights
enable companies to better plan improvement strategy and improve their
responsiveness.
The UDM also allows the option to define actions
in relation to query results. It provides a way to define actions that a
client can perform for a given context. This feature goes further than
traditional analytical applications which only present data.
Furthermore, it provides mechanism to discover problems and
deficiencies, thus improving the supply chain performance. An action can
start a specific application or load information from a database or a
data warehouse. For example, a drill-through action can show detailed
rows behind a total, or a reporting action can launch a report based on a
dimension attribute's value (parameters can be passed via URL).
Hyperlink actions can open specific pages or applications such as a web
page showing SCOR recommended best practices for particular process.
Actions can be specific to any displayed data, including individual
cells, dimension members, or KPIs, resulting in more detailed analysis
or even integration of the analysis application into a larger data
management framework.
After using the data mining for predictive
KPIs, it is possible to use different client applications and
technologies such as web portal dashboards, scorecard systems,
spreadsheets, web services, or feeds to display and analyze information.
Supply Chain Intelligence Web Portal for Predictive Analytics
A
predictive analysis solution is most effective when it is pervasive
throughout the organization and helps to drive day-to-day decisions
across the business with its scale and enterprise-level performance.
Furthermore, providing a way to implement comprehensive predictive
analysis intuitively enables self-service data mining for users, which
in turn enables the business to promptly gain actionable insights.
In
order to overcome the shortcomings of the existing BI and PM client
tools, the specialized web portal that enables supply chain users to
monitor business processes, collaborate, and take actions is designed. It has been successfully implemented as a pilot project in an
automotive company. Automotive supply chain is typically very complex,
with many organizations, intertwined processes, and multitude of users
with different requirements.
The portal represents a single point
of access to all relevant information in a personalized and secured
manner. Its composite and service-oriented architecture enables
inclusion of different PM components and tools (KPIs, dashboards,
scorecards, strategy maps, reports, etc.). PM elements can be
personalized and adjusted, and information can be filtered just by using
a web browser. PM elements can be defined within the portal and also
embedded from the external source (OLAP, another application, or
spreadsheets) via web services. This information is presented through
special analytical web parts. The portal itself can be a provider (via
web services or RSS-really simple syndication) to other applications.
All
these capabilities make the proposed PM model and the software system
extremely flexible and applicable in various supply chain scenarios and
different industries. Because they are based on the SCM metamodel and
use a standardized metrics they can be used in various business domains.
On the other hand, software system architecture enables relatively easy
and fast customization and extensibility. For example, existing KPI
components can be easily reused many times, and only needed items can be
added to web sites and pages. Also, new (custom) lower-level metrics
can be defined in the SCM metamodel and realized in the data warehouse,
thus allowing company-specific and industry-specific performance
measurement.
With PM portal capabilities, supply chain partners
and teams can do the following:
(i) use a predefined PM portal template with out-of-the-box modules optimized for access and management of reports, data connections, spreadsheets, and dashboards. Dashboard pages can contain several web parts, each of them showing information from different data sources.
(ii) communicate strategy and monitor its execution at different levels of the supply chain. KPIs status and trend can be tracked using the special KPI and scorecard web parts. KPIs can display information from different data sources (e.g., OLAP cubes or spreadsheets). The portal also supports the concept of strategy maps by providing a specialized module providing a hierarchical view of the KPI measures across levels of the organization by presenting relationships, priorities, and perspectives. Strategy map can be generated automatically, based on a particular scorecard. Each element on the map is highlighted with appropriate color. This enables visual performance tracking in relation to predefined strategy.
(iii) customize and
personalize sites, pages, or modules by adding or removing certain web
parts and by applying filter web parts. Filters allow dashboards to be
personalized by communicating shared parameters amongst web parts on a
dashboard. For example, the current user filter web part automatically
filters information based on who is logged on to the computer. This is
useful for display of personal information such as customer accounts or
tasks that is currently assigned to that user.
Figure 7 shows a specialized SCM scorecard for global supply chain performance management.
Figure 7 Supply chain scorecard with predictive KPI.
![](https://learn.saylor.org/pluginfile.php/5619163/mod_book/chapter/50117/528917.fig.007.png)
It
is constructed based on top of the OLAP KPIs, which are again based on
the SCM process model and metrics. KPI are created by SCM segments
(plan, source, make, deliver, and return) as hierarchies, so it is
possible to perform drill-down analysis, track performance against
defined goals, and get future performance values and trends.
The
presented solution is very flexible in terms of presenting the KPIs.
Owing to several specific BI web parts, the portal can display KPIs from
the OLAP server, spreadsheets, and other sources (portals, report
servers, etc.).
The dashboard page can display numerous metrics
and views business on a single screen. The portal supports quick
deployment of dashboards assembled from web parts. Each web part can
contain a particular view or metric, and users can customize their
individual dashboards to display the views that are most meaningful to
them, such as those with the metrics they need to monitor on a daily
basis.
Additionally, portal supports events and automatic
alerting. Users can subscribe to specific documents or keywords and
categories, to be notified (via email, SMS, or web feed) when metrics
are updated or new intelligence becomes available. They can also use
other features, such as planning, enterprise search, subscription, and
routing functions, to work with team members on a single item (i.e.,
scorecard, KPI, etc.) and to automate collaborative performance
management processes. The portal also provides fine-grained
authentication and authorization which enable secure access and content
personalization.
PM web portal enables business users to define
and use scorecards and key performance indicators to drive
accountability and alignment across the supply chain. Scorecards and
KPIs reflect planning, budgeting, and forecasting changes in real time
to help users understand the business drivers, challenges, and
opportunities they face. Monitoring becomes a part of the regular,
day-to-day management process.
Conclusions
The goal of supply chain performance management is
to help decision makers better manage, plan, understand, and leverage
their performance. Performance management includes monitoring,
measurement, and analysis of various performance data and also
collaborative decision making and synchronization.
Performance
management is critical to the ultimate success of complex business
systems such as supply chains. Key performance indicators are used to
measure supply chain performance on a strategic, tactical, and
operational level. Unfortunately, most of the existing KPI systems are
backward looking, isolated, and static. Also, they lack the ability to
efficiently deliver information to decision makers.
In the
fast-changing and volatile business environment where companies are
competing as part of supply chains, it is no longer sufficient to react
to problems after they occur, but to anticipate future performance and
intelligently recommend appropriate actions.
Predictive analytics
is a natural complement to traditional PM software and processes. While
most of existing supply chain PM systems present information about what
has happened, predictive PM systems can provide information about what
will happen and also why something happened and what should be done to
resolve performance problems.
The presented supply chain PM model
takes a unified approach to performance management with all the
elements required for the next generation of PM systems. The main
benefits of this approach and PM software solution can be summarized as
follows.
(i) Extracting additional value from existing data repositories: supply chain information systems hold a large volume of data. With predictive analytics, a new knowledge can be extracted, thus providing better projections about future performance.
(ii) Global approach to supply chain performance management: process model and metrics enables standardized performance measurement across all levels in supply chain hierarchy. Approach with data warehouse provides cleaned and consolidated data repository which can be used for data mining predictions.
(iii) Knowledge-based planning and strategy development: BI tools and technologies such as data mining and multidimensional analysis enable better management through more informed decision making. This provides enhanced scenario and risk analysis, improved planning, and ultimately development of optimal supply chain strategies.
(iv) Transition from reactive actions to proactive programs: employing predictive data mining models inside decision-making processes allows supply chain members to react timely and to better adapt to changes.
(v) Achieving a
competitive advantage application of predictive analytics can enable a
competitive advantage through better adaptivity, less risk, and improved
responsiveness.
(vi) Collaborative and pervasive intelligence and
performance monitoring: PM web portal provides a complete, intuitive,
and collaborative business ecosystem that extends the insight of
predictive analysis to inform business decisions throughout the supply
chain.
This makes the presented supply chain PM model and
software solution an excellent environment to create applications that
contain key features of future PM systems like visual intelligence,
collective intelligence, predictive analytics, and real-time insight
delivery.