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:

(i) directly related to overall strategy,
(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.



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.