Information Knowledge Systems Management

In an era of constant change paced by information technology, organizations are being forced to continually re-evaluate their competitive position and initiate timely change programs to assure their long-term viability and competitiveness. At the heart of these change initiatives are organizational processes, because ultimately the opportunities for establishing competitive discriminators invariably lie in how a product or service is created, marketed, sold, delivered, and supported. Today, all too frequently processes get redesigned without a total awareness of the impact on enterprise objectives. As a result, the redesigned processes often become disconnected from organizational objectives. This paper presents a methodology for designing, optimizing, managing, and adapting enterprise processes in change-driven dynamic business environments. This methodology is brought to life through the ProcessEdge Enterprise Suite and TeamEdge. The key benefits of the proposed methodology are achieving dramatic reduction in collaborative work cycle times and project risks through reuse of component processes successfully used in the past.

Organizations can more readily monitor and reposition their operations in fast-paced, competitive environments when their performance measurement systems provide them with relevant, timely, complete, and accurate information. Despite recognition of the need for such systems, much organizational performance measurement guidance consists primarily of descriptive approaches and anecdotes. There is a need for a structured approach to documenting and analyzing organization-wide performance measurement systems such as to address the organization, its environment, its measures, and its measurement infrastructure as an integrated whole. The purpose of this article is to describe a systems management approach and an associated process that have been developed to support the documentation and analysis of organization-wide performance measurement systems. A decision support system, The Organizational Performance Tracking & IMprovement Analysis System (OPTIMAS), was developed to automate use of the process and facilitate the documentation, analysis, evolution, and improvement of organizational performance measurement systems. It is described in this article. The approach and software emphasize identification of suppliers and customers, exogenous influences on organizational performance, the performance information infrastructure, and organizational components and processes. It requires detailed specification of performance measures and their logistical requirements. It supports analyses of individual metrics, sets of metrics sets, and the information infrastructure required for successful performance measurement. A case study illustrates use of the OPTIMAS process.

Recent years have seen increased efforts to link technology strategies to business strategies, which requires expressing returns on technology investments in terms of business impacts. These impacts are usually many years in the future and highly uncertain. These factors are typically addressed by applying discounted cash flow methods to valuing alternative investments. This approach often shows long-term technology investments to be of low present value because of the compounding of the discount rate several years into the future. Proponents of such investments often counter this heavy discounting by inflating projections. This debate can be recast by defining the purpose of technology investments, and the R efforts enabled by these investments, to be creation of options for achieving business results, not necessarily for creating business results directly. This article develops and illustrates this approach in terms of integrated models for options pricing, market/technology maturity, production learning, and competitive scenarios. These models are embodied in the Technology Investment Advisor, a computer-based tool that supports formulation and evaluation of technology strategies.

Decision Advisor® is a sophisticated knowledge-based “intelligent decision system” (IDS) that supports decision analysis and financial modeling of complex decisions. Decision Advisor partially automates deterministic and probabilistic analyses by coaching users through a structured analysis. Designed for strategic R decisions, the system also supports capital allocation, development, and business decisions outside the realm of R

Senior managers frequently make difficult strategic decisions that affect their entire organization. To do this, they often must rely on management experience and intuition. A growing arsenal of tools can be used to ensure that experience and intuition are appropriately applied and even enhanced by industry-wide best-practice knowledge, a comprehensive understanding of the firm's strengths and weaknesses, and an analytic assessment of how to prioritize implementation activities. This paper describes the use, development and evaluation of a tool called TOP-MODELER. We discuss the foundations of TOP-MODELER and the process of developing and validating its comprehensive knowledgebase. We also illustrate how TOP-MODELER has been applied to help streamline and clarify strategic and operations decisions for a wide array of strategists, designers, and managers in manufacturing (and other) organizations.