Proactive Supply Chain Performance Management with Predictive Analytics
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?
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.