Shubir Kapoor

A model-driven transformation method

Model-driven architectures (MDA) separate the business or application logic from the underlying platform technology and represent this logic with precise semantic models. These models are supposed to span the entire life cycle of a software system and ease the software production and maintenance tasks. Consequently, tools will be needed that support these tasks. In this paper, we present a method that implements model-driven transformations between particular platform-independent (business view) and platform-specific (IT architectural) models. On the business level, we focus on business view models expressed in ADF or UML2, whereas on the IT architecture side we focus on service-oriented architectures with Web service interfaces and processes specified in business process protocol languages such as BPEL4WS.

Model driven development for business performance management

Business process integration and monitoring provides an invaluable means for an enterprise to adapt to changing conditions. However, developing such applications using traditional methods is challenging because of the intrinsic complexity of integrating large-scale business processes and existing applications. Model Driven DevelopmentTM (MDDTM) is an approach to developing applications-from domain-specific models to platform-sensitive models-that bridges the gap between business processes and information technology. We describe the MDD framework and methodology used to create the IBM Business Performance Management (BPM) solution. We describe how we apply model-driven techniques to BPM and present a scenario from a pilot project in which these techniques were applied. Technical details on models and transformation are presented. Our framework uses and extends the IBM business observation metamodel and introduces a data warehouse metamodel and other platform-specific and transformational models. We discuss our lessons learned and present the general guidelines for using MDD to develop enterprise-scale applications.

RuleBAM: a rule-based framework for business activity management

This paper describes a rule-based framework for business activity management called RuleBAM. It is a novel framework whose objective is to support the requirements of dynamic monitoring and control of business applications using policies and rules. RuleBAM is composed of a customized assemblage of built-time and run-time technologies that include business rules and application modeling tools, code generators, transformation services, rules engines, business integration adaptors and Web services. Business activity management (BAM) polices are used to define the requirements of RuleBAM systems and business rules are exploited as execution platform for BAM policies. Taken as a whole, these form a new method of using business rules to enable business activity management. A case study is also presented in this paper.

A technical framework for sense-and-respond business management

In this paper we present a technical framework that supports sense and respond (SaR), the approach that enables an enterprise to adapt to a rapidly changing business environment. To implement the SaR approach, an enterprise proactively monitors trends and uses effective decision-support tools to help it act in a timely manner. We describe two pilot projects in which we implemented SaR prototypes and applied them to solve business problems. In the first pilot project we helped the IBM Microelectronics Division deploy an automated inventory management system based on our inventory optimization model. In the second pilot project, we helped the IBM Personal Computing Division deploy a SaR system in support of demand/supply conditioning. One of the components of this SaR system is an order trend model that provides early warning of constraints and excesses in the supply chain and helps make demand/supply conditioning more effective. Early results from these projects are encouraging and show that significant gains in profitability are possible.

Process information factory: a data management approach for enhancing business process intelligence

With access to critical performance indicators of business processes, executives, business managers and staff members can play a crucial role in improving the speed and effectiveness of an organizations business operations. The monitoring and analysis of business processes are complicated by the variety of organizational units and information systems involved in the execution of these processes. In this paper, we present a process information factory as a solution for managing performance data of business processes. The purpose of the process information factory is to provide a data foundation for a process-driven decision support system to monitor and improve business processes continuously.

Cross Enterprise Improvements Delivered via a Cloud Platform: A Game Changer for the Consumer Product and Retail Industry

Gaining visibility into their retail supply chain has become a top priority for the Consumer Product (CP) industry. However, taking a “do-it-yourself” approach to the problem is proving to be both expensive and complex. Cloud Computing, with its on-demand provisioning capability on shared resources, has emerged as a new paradigm to address the challenges of the CP industry. In this paper, we describe a framework for deployment of business analytic solutions on a Cloud platform. We illustrate the benefits of the approach in context of the Demand Driven Business Analytic solution that provides demand signals to CP manufacturers.

Discrete event simulation modeling of resource planning and service order execution for service businesses

In this paper, we present a framework for developing discrete-event simulation models for resource-intensive service businesses. The models simulate interactions of activities of demand planning of service engagements, supply planning of human resources, attrition of resources, termination of resources and execution of service orders to estimate business performance of resource-intensive service businesses. The models estimate serviceability, costs, revenue, profit and quality of service businesses. The models are also used in evaluating effectiveness of various resource management analytics and policies. The framework is aided by an information meta-model, which componentizes modeling objects of service businesses and allows effective integration of the components.

Sense-and-respond supply chain using model-driven techniques

The results of an effort to build a sense-and-respond solution for a supply chain by Using a model-driven development framework are described in this paper. One of the components of the framework is the IBM Research-developed model-driven business-transformation (MDBT) toolkit, a set of formal models, methods, and tools. The inventory optimization analytics used to improve supply chain performance are also described. This approach is illustrated through a case study involving the IBM System x™ supply chain.

Adaptive Project Risk Management

IT projects tend to be associated with over-budget and late deliveries. In this paper, we describe a method for improving project management based on (a) a thorough analysis of risks affecting activities in a project plan, i.e., the root factors leading to cost and time overruns, and (b) an optimization of the resources allocated to each activity in the project plan in order to maximize the probability of completing on time and within-budget. One key feature of our method is its capability to adapt and learn the risk factors affecting activities during the course of the project, which enables project managers to dynamically reallocate resources to ensure a better outcome given the updated risk profile.

Simulation of adaptive project management analytics

Typically, IT projects are delivered over-budget and behind schedule. In this paper, we explore the effects of common project management practices that contribute to these problems and suggest a better alternative that can utilize resources more effectively. Our alternative approach uses (a) a thorough analysis of risks affecting activities in a project plan (i.e., the root factors leading to cost and time overruns), and (b) an optimization of the resources allocated to each activity in the project plan to maximize the probability of on time and within budget project completion. One key feature of our method is its capability to adapt and learn the risk factors affecting activities during the course of the project, enabling project managers to reallocate resources dynamically to ensure a better outcome given the updated risk profile. We use simulations to test the performance of our optimization algorithm and to gain insights into the benefits of adaptive re-planning.