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?
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.