The primary focus of the research is the application of computer technology to the field sales function. A review of the literature, a survey of an industry and a study of a geographic market indicate the general lack of direct computer support currently available to the field salesman. At the same time, selling costs are increasing and capable sales people are scarce. Computer support that results in better sales calls meets an existing and important need. A model of the sales decision process was developed. It addresses the two major questions all field salesmen ask: on whom to call and how to prepare. The information content of the model was identified and a decision support system was designed and developed to provide the needed data. A number of organizations were studied to test the implementations. These case studies formed the basis for the results. The computer based system collects, maintains and retrieves data on customers, prospects and sales calls. This information helps the salesman to select daily those companies on which to call and to better prepare for those calls. Management control of sales activity is improved and the data becomes a company resource. The system does not make decisions for the salesman. It does provide information to help the salesman manage his territory and it offers continuity to the organization when responsibility is transferred. The research also investigates the behavior of system users and the iterative nature of the design process. The system provides a convenient vehicle to do this as its five implementations have spanned four and a half years and have included six organizations, seventeen salesmen and real-time, batch and service bureau modes of operation.

published_or_final_version / Business Administration / Master / Master of Business Administration

published_or_final_version / Management Studies / Master / Master of Business Administration

published_or_final_version / Business Administration / Master / Master of Business Administration

The healthcare sector is facing a significant challenge: delivering quality clinical care in a costly and intricate environment. There is a general consensus that a solution for many aspects of this problem lies in establishing a framework for effective and efficient clinical decision support. The key to good decision support is offering clinicians just-in-time accessibility to relevant patient specific knowledge. However, at the present time, management of clinical knowledge and patient records is significantly inadequate resulting in sometimes uninformed, erroneous, and costly clinical decisions. One of the contributing factors is that the field of healthcare is characterized by large volumes of highly complex medical knowledge and patient information that must be captured, processed, interpreted, stored, analyzed, and exchanged. Moreover, different clinical information systems are typically not interoperable. This thesis introduces an approach for realizing a clinical decision support framework that manages complex clinical knowledge in a form of evidence-based clinical practice guidelines. The focus of presented work is directed on the interoperability of knowledge, information, and processes in a heterogeneous distributed environment. The main contributions of this thesis include definition of requirements, conceptual architecture, and approach for an interoperable clinical decision support system that is stand-alone, independent, and based on open source standards.

Knowledge-Based Model Construction (KBMC) has generated a lot of attention due to its importance as a technique for generating probabilistic or decision-theoretic models whose range of applicability in AI has been vastly increased. However, no one has tried to analyze the essential issues in KBMC, to determine if there exists a general efficient KBMC method for any problem domain, or to y identify the fruitful future research on KBMC. This research presents a unified framework for comparative analysis of KBMC systems identifying the essential issues in KBMC, showing that there is no such general efficient KBMC method, and listing the fruitful future research on KBMC. This thesis then presents a new KBMC mechanism for hierarchical diagnosis and repair. Diagnosis is formulated as a stochastic process and modeled using influence diagrams. In the best case using an abstraction hierarchy in problem-solving can yield an exponential speedup in search efficiency. However, this speedup assumes backtracking never occurs across abstraction levels. When this assumption fails, search may have to consider different abstract solutions before finding one that can be refined to a base solution, and, therefore, search efficiency is not necessarily improved. In this thesis, we present a decision model construction method for hierarchical diagnosis and repair. We show analytically and experimentally that our method always yields a significant speedup in search efficiency, and that hierarchies with smaller branching factors yield more significant efficiency gains. This thesis employs two causal pathways (functional and bridge fault) of domain knowledge in device trouble shooting, preventing either whole class of faults we will never be able to diagnose. Each causal pathway models the knowledge of adjacency and behavior within the corresponding interaction layer. Careful search of causal pathways allows us to restrict the search space of fault hypotheses at each time. We model this search among causal pathways decision-theoretically. Decision-theoretic control usually results in significant improvements over unaided human expert judgments. Furthermore, these improvements in performance are robust to substantial errors in the assessed costs and probabilities. / Graduation date: 1995

The capital budgeting process is conducted in a dynamic, uncertain environment. In each period of the process, a manager has only estimated values for system parameters, project costs, and project returns. The manager must consider project firm's capital among interdependencies the available in allocating the projects. After completing the allocation process in one period, the chosen projects are funded until the end of the next period. These projects are then considered along with new projects and the process is repeated again. The capital budgeting decision in one period is therefore only one of a long sequence of such decisions, all of which are made in a dynamic, uncertain environment. The algorithm presented in this study models the dynamic environment of uncertainty. The algorithm utilizes a future worth of net return criterion in conducting the decision. Available projects may be estimated as discrete point estimates or as combinations of continuous functions. All projects under consideration need not have the same life; unequal-lived projects may be considered. After the optimal combination of projects is identified, four sensitivity analyses may be run to analyze the effect of any uncertainty in that period. The dynamic environment may then be analyzed by simulating the environment which would be faced when the decision is made .again at the end of the next period. Any of the system parameters and estimates of the continuing projects may be altered in that period to reflect the changes in the last period's estimates. An example is provided to illustrate the workings of the algorithm. / M.S.

The concept of a generalized model management System (GMMS) and its role in a decision support system are discussed. A paradigm for developing a GMMS which integrates artificial intelligence techniques with data management concepts is presented. The paradigm views a GMMS as a knowledge-based modeling system (KBMS) with knowledge abstractions as the vehicle of knowledge and model representation. Knowledge abstractions are introduced as a hybrid of the predicate calculus, semantic network, and frame representations in artificial intelligence (AI) embodied in an equivalent of a programming language data abstraction structure. As a result, models represented by knowledge abstractions are not only subject to the powerful problem reduction and inference techniques available in the AI domain but are also in a form conducive to model management. The knowledge abstraction in its most general form is seen as a frame which serves as a template for generating abstraction instances for specific classes of models. The corollaries of an abstraction-based GMMS with current data management concepts are explored. A CODASYL implementation of an abstraction-based GMMS for the class of linear programming models is described and demonstrated.

Call number: LD2668 .T4 1986 F63 / Master of Science / Computing and Information Sciences

M.B.A. / Electronic Data Systems (EDS) Africa is part of EDS International, one of the largest outsourcing companies in the world. EDS's clients expect EDS to have the required knowledge and decision-making skills to ensure that their Wide area NETWORK (WAN) redundancy is in line with the business requirement. This is a careful balance between spending money on redundancy compared with the cost of WAN downtime to the business. This implies that EDS's management must realise when the WAN design is not optimal. A decision must then be made to change the design to incorporate the correct redundancy. EDS Africa incurs substantial costs related to services supplied by Telkom (the only Telecommunication company allowed to rent WAN data lines to customers in South Africa at this point in time. They are owned by the government.). At present EDS is unable to measure Telkom's up-times using proper historical data, and using applied business statistics. (Tony Webster, 1998). Telkom does not provide EDS with any service level guarantees. When a company out-sources business to EDS, the two parties usually sign a Service Level Agreement (SLA). This agreement will state the network up-time and conditions of conducting business between the two parties. EDS generally excludes Telkom problems from the SLA, as they cannot be held responsible for problems that are outside of their control...