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.