Supply Chain Business Intelligence Model

Nenad Stefanovic1, Vidosav Majstorovic2, Dusan Stefanovic1 1Information Systems Division, Zastava Automobiles, Inc., Kragujevac, Serbia and Montenegro

2Laboratory for Production Metrology and TQM, Mechanical Engineering Faculty, University of Belgrade, Serbia and Montenegro

3Department for Informatics, Faculty of Science, University of Kragujevac, Serbia and Montenegro

Abstract This paper discusses the need for Supply Chain Business Intelligence and driving forces for its adoption, and presents BI development life cycle along with quality success factors. Also, Internet-based supply chain BI model that enables many-to-many, loosely-coupled information exchange for collaborative business analysis and decision making is described. Finally, the main elements of the developed BI solution and Enterprise Portal based on the native web technologies will be presented.

Keywords SCM, BI, Model

1 INTRODUCTION The demands for a high quality product and services are rising. Customers want the right product, at the right place and in timely fashion. Modern manufacturing has driven down the time and cost of the production process, leaving supply chains as the final frontier for cost reduction and competitive advantage. In such environment organization can not be viewed as a single business entity, but rather as a part of the supply chain that is competing with other chains on the market [1]. Supply chain management (SCM) was seen as an opportunity for cost reduction through optimization, and real-time collaboration with trading partners. Given the increasing competition in today’s tough business climate, it is vital that organizations provide cost-effective and rapid access to business information for a wide range of business users, if these organizations are to survive into the new millennium. The solution to this issue is a business intelligence (BI) system, which provides a set of technologies and products for supplying users with the information they need to answer business questions, and make tactical and strategic business decisions. Data is an asset to any organization. However, its value is realized only if it is in a ready to use form. Hence, gathering, managing and utilizing data has been a major activity for all the organizations over the years. Giving a meaningful shape to disparate sources of data lying at different operational systems, databases and applications, is very difficult task. Being able to consolidate and analyze this data for better business decisions can often lead to a competitive advantage, and learning to uncover and leverage those advantages is what business intelligence is all about. By providing wider visibility to plans and supporting data, BI tools increase the return on existing SCM applications because they help companies understand where and how they deviate from their plan objectives. In addition, they provide shared data availability that encourages a global perspective on business performance. Supply Chain Intelligence (SCI) is a new initiative that provides the capability to reveal opportunities to cut costs, stimulate revenue, and increase customer satisfaction by utilizing collaborative decision making [2]. SCI takes broader, multidimensional view of supply chain in which, using patterns and rules, meaningful information about the data can be discovered. The following megatrends can be identified [3]:

• Information democracy - Companies are putting business intelligence tools and dashboards in the hands of hundreds of white-collar employees, not just a few marketing or financial analysts.

• Unstructured data - Tomorrow's data warehouses will have free-form text -- like notes from the call centre agent about why the customer hated your product -- and even images.

• Predictive analytics - Tools that can predict what your customers are likely to buy, and when they're likely to defect, will be extremely powerful.

• Integration - BI software will be blended into regular operations to the point where managers will be able to monitor business activity throughout the day and some business decisions will be automated.

Regulatory concerns and an increasing quantity of data caused BI to retain the top spot in planned purchases. Demand for financial applications also stayed on top in 2005 with 4 percent growth [4]. There is also trend of recognizing information as a strategic part of the business [5]. Companies are implementing enterprise-wide BI into other key enterprise projects that promise to optimize business processes and deliver benefits to the bottom line. Businesses collect large quantities of data in their day-to-day operations: data about orders, inventory, accounts payable, point-of-sale transactions, and of course, customers. In addition, businesses often acquire data, such as demographics and mailing lists, from outside sources. Being able to consolidate and analyze this data for better business decisions can often lead to a competitive advantage, and learning to uncover and leverage those advantages is what business intelligence is all about. Some examples are: • Achieving growth in sales, reduction in operating

costs, and improved supply management and development.

• Using OLAP to reduce the burden on the IT staff, improve information access for business processing, uncover new sources of revenue, and improve allocation of costs.

• Using data mining to extract key purchase behaviors from customer survey data.

When implemented, a BI system should help decision makers extend information access and analysis capabilities to a broader user base, as well as capture and share individual expertise to benefit the enterprise.

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2 BI DEVELOPMENT

2.1 BI development life cycle Almost every kind of engineering project—structural engineering as well as software engineering—goes through six stages between inception and implementation: Stage 1. Justification: Assess the business need that gives rise to the new engineering project. Stage 2. Planning: Develop strategic and tactical plans, which lay out how the engineering project will be accomplished and deployed. Stage 3. Business analysis: Perform detailed analysis of the business problem or business opportunity to gain a solid understanding of the business requirements for a potential solution (product). Stage 4. Design: Conceive a product that solves the business problem or enables the business opportunity. Stage 5. Construction: Build the product, which should provide a return on investment within a predefined time frame. Stage 6. Deployment: Implement or sell the finished product, then measure its effectiveness to determine whether the solution meets, exceeds, or fails to meet the expected return on investment. BI development steps are shown in Figure 1. They span the whole product development lifecycle from business case assesment to implementation.

Design

ETL Design

Justification

Business CaseAssesment

Planning

Project Planning

Enterprise Infrastructure

Evaluation

Business Analysis

Project Requirements Definition

Data AnalysisApplication Prototyping

Meta Data Repository Analysis

Database Design Meta Data Repository Design

Construction

Application Development

Data MiningETL Development

Meta Data repository Development

Deployment

Implementation

Release evaluation

Figure 1: BI Development Stages

Because there is a natural order of progression from one engineering stage to another, certain dependencies exist between some of the development steps. Steps stacked on top of each other in the diagram are performed relatively linearly (with less overlap) because of their dependencies, while steps that appear to the right or left of each other can be performed simultaneously. Since BI is an enterprise-wide evolving environment that is continually improved and enhanced based on feedback from the business community, the system development practices of the past are inadequate and inappropriate. Unlike static stand-alone systems, a dynamic, integrated BI decision-support environment cannot be built in one big bang. Data and functionality must be rolled out in iterative releases, and each deployment is likely to trigger new requirements for the next release. A waterfall methodology is not suitable for the iterative releases of BI decision-support applications, but an agile and adaptive development guide specifically geared toward BI decision-support applications is. While some development steps are clearly project-specific, most development steps must be performed from a cross-organizational perspective. Thus the focus of those project activities takes on a cross-functional dimension, and the reviewers of those activities should include business representatives from other lines of business. The main task for the business representatives from the other lines of business is to validate and ratify the strategies, policies, business rules, and standards either being used or being developed during the BI project. Building a BI decision-support environment is a never-ending process. Unlike most operational systems, which have sharply defined functionalities, BI applications must evolve to handle emerging information needs. As the needs and goals of your organization change, so must the BI decision-support environment. There is no practical way to anticipate all possible questions in the initial design of the BI decision-support environment or of any BI application. The best you can do at any given time is to have an environment that supports the current organizational goals and that can be easily adapted to new goals. Plan to design flexible and easy-to-change BI applications so that you have the ability to modify them when the organization's goals change. This applies to all BI initiatives, from small departmental data marts to large industrial-strength enterprise data warehouses. Be prepared to modify all BI applications and BI target databases in future releases in order to provide new query and reporting capabilities and more data.

2.2 Critical factors for a BI Solution The most important guidelines for achieving project success are [6]: • Scope the project to be able to deliver within at least

six months. • Select a specific business subject area; do not try to

solve all business requirements within one project. • Find a sponsor from the upper management of the

business side of the company. • Involve the sponsor throughout the project. • Establish a sound information and communication

structure that includes business and technical staff inside and outside the project.

• Define the contents and type of the deliverables of the project as early and in as much detail as possible.

• Together with the end users validate the results of the analysis phase (the initial dimensional models) against the deliverables definition.

• Deploy the solution quickly to a limited audience and iterate development.

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• Establish commonly agreed on business definitions for all items within the scope of the project.

• Validate the quality and correctness of the information before making it available to the end user community.

• Keep the end users involved and informed throughout the project.

• Be prepared for political and cultural obstacles between business departments or between business and IT departments.

Also, there are a number of success indicators such as: • Return on investment (ROI) – Lower costs, improved

productivity, increased revenue. • The data warehouse utilization. • The data warehouse usefulness. • The project is delivered on time. • The project is delivered within budget. • There is improved user satisfaction. • There are additional requests for data warehouse

functions and data. • Goals and objectives are met. • Business opportunity is realized. • Business performance-based benchmarks. Some of the indications of failure are: • Users are unhappy with the quality of the data. • Project is out of budget. • Only a small percentage of users take advantage of

the data warehouse. • Users are unhappy with the analytical tools. • Integration is not achieved.

3 SUPPLY CHAIN BI MODELING In order to achieve SCI objectives we need a supply chain-wide business excellence model that will provide consistent framework for establishing, modelling, managing, measuring, and improving supply chain processes. SCI model is based on the global supply chain excellence which unifies the business domain (modelling, people, existing supply chain and business process models, best practices, and quality management models), and the functional domain (information technology infrastructure, modern object-oriented development methods, and patterns) [7] and methodology for supply chain process integration [8].

3.1 Process Approach Processes are important assets. They are a company’s core competencies and determine business performance. Managing and measuring business processes is critical for SCI. Supply Chain Operations Reference (SCOR) model integrates the well-known concepts of business process reengineering, benchmarking, and process measurement into a cross-functional framework, and represents an industry standard [9]. SCOR model is also very useful in achieving a common understanding of SCM domain from the software agent point of view [10]. It contains standard descriptions of management processes, a framework of relationships among the standard processes, standard metrics to measure process performance, and management practices that produce best-in-class performance. SCOR is based on five distinct management processes: plan, source, make, deliver, return. Thanks to the standardization and metrics system, partners in supply chain can communicate more unambiguously, collaboratively measure, manage, and control their

processes, and establish benchmarking for performance comparison and uncover best business practices for gaining competitive advantage. However, SCOR does not address important areas such as organization-wide training and development, tools and methodologies focused on process execution, project management, and problem solving techniques [11]. These problems can be surpassed by incorporating different quality management initiatives like ISO 9000, Lean, and Six Sigma, thus creating comprehensive integrated management system.

3.2 Modelling and Design Methodology For every supply chain, it is essential to make a model of the business. Models provide visualization (visualize final situation, show relationships between “objects”), complexity management (focus on one aspect at a time, reuse patterns/objects), and communication (standard symbols, go in details). A business model is composed of the views, diagrams, and objects and processes. It helps to better understand the key mechanisms of an existing supply chain, to act as the basis for creating suitable information systems that support the business, to act as the basis for improving the current business structure and operation, and decision-making. Modelling and analysing the logistic interdependencies across supply chains enables supply chains to better control their process reliability [12]. Modelling approaches are essential for developing and benchmarking autonomous logistic processes [13]. However, the traditional modelling techniques and notations do not satisfy modelling demands of complex processes found in supply chains. An advantage of modelling in a language such as UML (Unified Modelling Language) is that it visually depicts functions and relationships that are usually difficult to visualize clearly and offers standard notation throughout lifecycle, both for the business people and the software specialists. The UML consists of nine different diagram types, and each diagram shows a specific static or dynamic aspect of a system. Using the technologies based on XML such as XMI (Extensible Model Interchange), models can be exchanged among different teams across the supply chain and different CASE (Computer Aided Software Engineering) tools. Common Warehouse Metadata (CWM) addresses the metadata definition issue for the business intelligence field, including OALP, data mining, transformation, and so on [14]. It is also based on UML. Using the object-oriented modelling techniques to describe the business has several advantages: concepts similar to real-life, well-proven established techniques, standard notation, short learning curve, and new and easier ways to view an organization or a business. We based our supply chain modelling on UML, and techniques like RUP (Rational Unified Process) [15] and EUP (Enterprise Unified Process) which includes new disciplines and phases, and should be tailored into the standard RUP, making it more effective [16]. RUP uses the iterative approach and it allows to be extended and configured for the particular use.

3.3 SCI Model The proposed modelling approach enables the creation of RUP plug-in tailored for SCM and based on the SCOR. With this process framework supply chain processes can be modelled. Each supply chain can decide how much of the process to implement and which roles, activities, artefacts, and workflows it will use - Business modeling, Requirements, Development & Analysis, Implementation,

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Test, Deployment, Project management, Configuration and Change Management, and Environment. The global SCI model is shown in Figure 2. It starts with defining business objectives and eliciting requirements, and followed with the creation of supply chain configuration based on SCOR Metamodel. We have created the SCOR Metamodel (Figure 3) which enables creation of any supply chain configuration and it is the basis for further modelling. The Metamodel is normalized and contains all SCOR elements such as processes, metrics, best practices, inputs and outputs. It also incorporates business logic through relationships, cardinality, and constrains. The object model and UML notation allow us to use object-oriented techniques such as inheritance, encapsulation, abstraction, generalization, etc. Also, patterns can be identified that offer model and software reuse. The Metamodel is extended with additional entities to support supply network modelling. That way, processes, metrics and best practices can be related to specific nodes and tiers in the supply network. SCOR defines processes in three levels of details. With this Metamodel, lower-level processes also can be modelled thus providing more detailed view of supply chain processes. In the next phase, further modelling is performed in relation to specific business subject. The resulting artefacts are different types of UML diagrams (use case, activity, class, component, database, etc.) which are basis for the operational data store (ODS), ERP applications, and SCI solutions. After we have created supply chain model from our metamodel (we have metrics system for each of the process level and the nodes) and forward engineered ODS from the model, the next step is to design data warehouse metadata. Now, we can design star schemas

and snowflakes with particular fact tables, dimensions, measures, hierarchies, and aggregations. BI Metadata is very important as it represents the foundation for a further analysis. Since the BI is domain specific, it is necessary to involve business analysts during the modelling in order to ensure that metadata supports the real supply chain. The well designed and collaboratively maintained metadata ensures the data quality and provides a single version of the truth.

Figure 2: SCI Global Model.

The last step is creating the front-end analysis applications such as KPI (Key Performance Indicator) systems, balance scorecards, reporting systems, and data mining solutions. These tools provide end-users with predefined and ad-hoc reports, help them to measure and monitor progress toward organizational goals, and discover meaningful information about the data. When being web-enabled they can be consumed by any client application and platform.

Figure 3: SCOR Metamodel.

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4 SUMMARY Automotive industry, as one of the most complex, faces considerable challenges. SCI can help supply chain participants to integrate their systems and achieve a sophisticated level of data analysis and control of their information bases. The proposed SCI model is used to develop the SCI solution in one big Serbian automotive company. This company has typical supply chain which consists of six internal factories and many external partners. The objective was to create SCI solution that would provide collaborative and global analysis and decision making. The existing system infrastructure which included one huge central relational database for internal factories, but also the data in different sources (flat files, spreadsheets, raw files, and databases) had to be taken into account. The proposed model allow us to configure the design processes according to concrete business requirements, but also to design a solution which can be easily changed and scaled in the future. This comprehensive, integrated approach helps to seamlessly build and deploy robust business intelligence applications to transform information into better business decisions across the supply chain and at all organizational levels. These kinds of projects are very complex and multidisciplinary, with many of the technical details, so only main characteristics will be presented. The project started with the following steps: • Project management plan, Team creation, and

Requirements definition. • Supply chain modelling based on SCOR Metamodel. • Detailed modelling with UML and according to the

unified process. The knowledge gained from the previous modelling represent the inputs for the further SCI design: ETL packages, OLAP cubes, facts, dimensions, measures, hierarchies, KPIs, and reports. Some of the interesting and useful features of the solution are: • Allows business rules to be captured in the model to

support richer analysis. • Allows the user model to be greatly enriched. • Supports real-time analysis using the proactive

caching. • Hierarchies which are imply a sequence of attributes

that can then be used in queries to ease such drill-down/drill-up scenarios.

• Measures, hierarchies, and other objects are grouped into folders meaningful to the user, allowing the reporting tool to display large numbers of attributes in a manageable way.

• Allows translations of metadata to be provided in any language.

• Perspectives, each one presenting only a specific subset of the model (measures, dimensions, attributes, and so forth) that is relevant to a particular group of users.

• KPI system where each performance metric contains: actual value, goal, value, status, and trend as shown in Figure 5.

• Users can take action based upon the data they see • Security: Roles can be defined, permissions granted to

the roles, and users included as members of each role.

Figure 5: KPI System

Figure 6: SCI Web Portal

• SCI web portal with main features (Figure 6.): role-based security, customization, personalisation, support for collaboration, document exchange, connection to different data sources (cubes, databases, XML web services), views, analytical tools such as ad-hoc multidimensional queries (MDX), cell colouring, drilldown, etc.

5 SUMMARY Business intelligence has become essential in most organizations. BI is not constrained to individual departments or organizations, but rather is viewed as essential at the supply chain level with many organizations now focusing on growing their BI maturity vis-a-vis prior states as well as peer organizations. This paper analyzed the need for supply chain BI and presented the development methodology that incorporate iterative and cross-functional approach. The introduced SCI model promotes the unified approach for supply chain modelling and BI design according to real business requirements. It reduces the development lifecycle by optimizing the iterative and incremental delivery mechanism and workflow and utilizes best practices and standards in formulating the processes in supply network. Supply chain standard process reference model with its metrics and best practices, quality management, and object-oriented modelling provide extensive knowledge which can be used in the design of data warehouse metadata, data mining models and multidimensional analysis. Supply chain business intelligence reveals opportunities to reduce costs and stimulate revenue growth and it enables companies to understand the entire supply chain from the customer’s perspective.

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[8] Stefanovic, D., Majstorovic, V., Stefanovic, N., 2005, Methodology for Process Integration in Supply Networks, The 38th CIRP ISMS, Florianopolis, Brazil.

[9] Supply-Chain Operations Reference-Model Overview Version 7.0, 2005, Supply Chain Council, www.supply-chain.org.

[10] Scholz-Reiter, B., Höhns, H., Hamann, T., 2004, Adaptive Control of Supply Chains: Building blocks and tools of an agent-based simulation framework, Annals of the CIRP, 53/1:353.

[11] [Recker, R., Bolstroff, P., 2003, Integration of SCOR with Lean & Six Sigma, Advanced Integrated Technologies Group, Inc.

[12] H-P. Wiendahl H.-P., von Cieminski, G., Begemann, C., 2003, A Systematic Approach for Ensuring the Logistic Process Reliability of Supply Chains, Annals of the CIRP, 52/1:375.

[13] Scholz-Reiter, B., Freitag, M., de Beer, C., Jagalski, T., 2005, Modelling Dynamics of Autonomous Logistic Processes: Discrete-event versus Continuous Approaches, 54/1:413.

[14] The Common Warehouse Metamodel, Object Management Group, Inc., MA, www.omg.org/cwm.

[15] Rational Unified Process Home Page, 2004, IBM, www.rational.com/products/rup/index.jsp.

[16] Ambler W.-S., Nalbone, J., Vizdos, J.-M., 2005, The Enterprise Unified Process: Extending the Rational Unified Process, Pearson Education, Prentice Hall PTR, Upper Saddle River, New Jersey.

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