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.