A decision support system for real-time order management in a heterogeneous production environment

In today's competitive market, many companies are morphing from the traditional new build, single brand, and silo environments to facilities accommodating diverse business missions. The later are called heterogeneous production environments in which the different business channels share their final production stage (shipping) to enable competitive advantages. In these production environments, at the operational level, the critical success factors are customer satisfaction, on-time delivery, product complexities, supply allocation, and resource utilization. At the strategic level, the success factors are revenue, customer urgency, and sales impact. This study proposes an End-to-End Customer Order Management System (E2E COMS) focusing on effective utilization of individual and shared resources to support real-time order management and mitigate risk of managing diverse missions. The proposed system consists of three integrated tools: Order Prioritization Tool (OPT) to assess and prioritize customer orders for each business channel, Order Fulfillment Progress Projection Tool (OFPPT) to predict the expected remaining order completion time considering inventory and resource capacity constraints, and risk mitigation tool to assess the risk of missing an order shipment due to shipping constraints. A real-time dashboard is developed to visualize the prioritized customer orders, expected time to arrive at the shipping area, shipping instructions, and two-dimensional risk assessment charts. The proposed system can effectively be used for shipping capacity management as well as prompt decision making.     

A fuzzy, knowledge-based decision support tool for production operations management

Abstract: Production operations managers frequently have to make decisions based on vague, imprecise knowledge. Any software tool developed to aid their decision making needs to take into account the approximate nature of the information available to them and the inexact knowledge to which individual facts are applied. Much of this knowledge is expressed as vague, linguistic articulations. A convenient framework for dealing with such approximate knowledge is fuzzy logic and fuzzy set theory. As a specific example, a system was developed for providing decision support in the Just-in-Time area of production operations management.     

A knowledge based decision support system for computer performance management

Computer systems managers make decisions about hardware and software selection, performance evaluation, capacity planning, and other resource variables on the basis of factual data, accounting data, subjective judgements, and assumptions about the resource consumption of the jobs being run. The importance of computer resource planning calls for effective support methods. A Knowledge-Based DSS (KBDSS) will be able to assist managers in making these policy decisions by utilizing knowledge of the existing configuration and its capabilities, the organizational computing environment, available external resources, and their suppliers. Combining procedural and declarative methods, such a KBDSS may provide early warning of possible bottlenecks, forecast growth of hardware usage, and employ knowledge based inferencing to suggest suitable remedial actions to the systems manager. This paper presents a KBDSS for supporting computer resource planning decisions using a procedural/declarative framework, and illustrates the system's usage aspects.     

A multi-agent system for meteorological radar data management and decision support

The continuous processing and evaluation of meteorological radar data require significant efforts by scientists, both for data processing, storage, and maintenance, and for data interpretation and visualization. To assist meteorologists and to automate a large part of these tasks, we have designed and developed Abacus, a multi-agent system for managing radar data and providing decision support. Abacus' agents undertake data management and visualization tasks, while they are also responsible for extracting statistical indicators and assessing current weather conditions. Abacus agent system identifies potentially hazardous incidents, disseminates preprocessed information over the web, and enables warning services provided via email notifications. In this paper, Abacus' agent architecture is detailed and agent communication for information diffusion is presented. Focus is also given on the customizable logical rule-bases for agent reasoning required in decision support. The platform has been tested with real-world data from the Meteorological Service of Cyprus.     

Model management system for IRT-based test construction decision support system

Decision support system (DSS) has become widespread for some specific domains in recent years. However, DSS for IRT-based (item response theory) test construction has not yet been developed. This domain basically imposes a semi-structured or unstructured decision and, therefore, involves a very complex modeling process. This study develops a model management system (MMS) architecture to assist a non-expert user in manipulating test construction process efficiently and effectively. This architecture consists of four components: problem analysis, model type selection, model formulation and solver. The model type selection subsystem is further organized into three levels of hierarchy, i.e., environment, structure and parameter. A prototype is presented to demonstrate the feasibility of this architecture. The results indicate that this approach can be applied for providing an integrated, flexible and user-friendly DSS environment for producing better quality of results in less solution time.     

Development of medical decision support system for leukemia management

A prototype Medical Decision Support System (MDSS) for leukemia patients was developed with emphasis on total management approach from patient registration to diagnosis and treatment. Thus, the MDSS consists of four modules: registry, knowledge model, simulator, and Computer-Assisted Instruction (CAI). Integration of each module improves overall patient management capability and knowledge acquisition capability of the system. Four different knowledge models were developed to predict diagnosis: rule-based reasoning, case-based reasoning, neural network, and discriminant analysis. Among the four, rule-based reasoning produced the most accurate prediction in diagnosis. In the future, the method of leukemia registry can further be extended to the hospital-based cancer registry for other types of cancer. In order to be more effective, the registry should also be integrated with the hospital information system for an easier data entry.     

A real-time production operations decision support system for solving stochastic production material demand problems

Nowadays, shop floor managers are facing numerous unpredictable risks in the actual manufacturing environment. These unpredictable risks not only involve stringent requirements regarding the replenishment of materials but also increase the difficulty in preparing material stock. In this paper, a real-time production operations decision support system (RPODS) is proposed for solving stochastic production material demand problems. Based on Poon et al. (2009), three additional tests are proposed to evaluate RFID reading performance. Besides, by using RPODS, the real-time status of production and warehouse operations are monitored by Radio Frequency Identification (RFID) technology, and a genetic algorithm (GA) technique is applied to formulate feasible solutions for tackling these stochastic production demand problems. The capability of the RPODS is demonstrated in a mould manufacturing company. Through the case study, the objectives of reducing the effect of stochastic production demand problems and enhancing productivity both on the shop floor and in the warehouse are achieved.     

A decision support system for production scheduling in an ion plating cell

Production scheduling is one of the major issues in production planning and control of individual production units which lies on the heart of the performance of manufacturing organizations. Traditionally, production planning decision, especially scheduling, was resolved through intuition, experience, and judgment. Machine loading is one of the process planning and scheduling problems that involves a set of part types and a set of tools needed for processing the parts on a set of machines. It provides solution on assigning parts and allocating tools to optimize some predefined measures of productivity. In this study, Ion Plating industry requires similar approaches on allocating customer's order, i.e. grouping production jobs into batches and arrangement of machine loading sequencing for (i) producing products with better quality products; and (ii) enabling to meet due date to satisfy customers. The aim of this research is to develop a Machine Loading Sequencing Genetic Algorithm (MLSGA) model to improve the production efficiency by integrating a bin packing genetic algorithm model in an Ion Plating Cell (IPC), such that the entire system performance can be improved significantly. The proposed production scheduling system will take into account the quality of product and service, inventory holding cost, and machine utilization in Ion Plating. Genetic Algorithm is being chosen since it is one of the best heuristics algorithms on solving optimization problems. In the case studies, industrial data of a precious metal finishing company has been used to simulate the proposed models, and the computational results have been compared with the industrial data. The results of developed models demonstrated that less resource could be required by applying the proposed models in solving production scheduling problem in the IPC.     

A knowledge-based decision support system for the management of parts and tools in FMS

Flexible manufacturing systems (FMS) are very complex systems with large part, tool, and information flows. The aim of this work is to develop a knowledge-based decision support system (KBDSS) for short-term scheduling in FMS strongly influenced by the tool management concept to provide a significant operational control tool for a wide range of machining cells, where a high level of flexibility is demanded, with benefits of more efficient cell utilization, greater tool flow control, and a dependable way of rapidly adjusting short-term production requirements. Development of a knowledge-based system to support the decision making process is justified by the inability of decision makers to diagnose efficiently many of the malfunctions that arise at machine, cell, and entire system levels during manufacturing. In this context, this paper proposes three knowledge-based models to ease the decision making process: an expert production scheduling system, a knowledge-based tool management decision support systems, and a tool management fault diagnosis system. The entire system has been created in a hierarchical manner and comprises more than 400 rules. The expert system (ES) was implemented in a commercial expert system shell, Knowledge Engineering System (KES) Production System (PS).     

PASIM: A spreadsheet-based decision support system for performance analysis of production workers

Electronic spreadsheets offer low cost approach to information management and implementation of decision models for online decision support systems. In this paper, we have combined quantitative models, database management, and simulation to develop a decision support system for real-time performance monitoring of production workers.     

Evaluation of a decision support system for credit management decisions

This study was conducted to evaluate the effectiveness of a decision support system (DSS) for credit management. This study formed a part of a larger initiative to assess the effectiveness of IT-based credit management processes at the State Bank of India (SBI). Such a study was necessitated since credit appraisal has emerged to become a critical sub-function in Indian banks in view of growing incidence of non-performing assets. The DSS that we assessed was a credit appraisal system developed in Quattro Pro® at SBI. This system helps in the analysis of balance sheets, calculation of financial ratios, cash flow analysis, future projections, sensitivity analysis and risk evaluation as per SBI norms. We used a strong quasi-experimental design, called the Solomon's four-group design, for our assessment. In our experiment, managers of SBI who attended training programs at the SBI training college, were the subjects. The experiment consisted of measurements that were taken as pre- and post-tests. An experimental intervention was applied between the pre-tests and the post-tests. The intervention, or stimulus, consisted of DSS training and use. There were four groups in our experiment. The stimulus remained constant as we took care to ensure that the course contents as well as the instructors remained the same during the course of our experiment. Two were experimental groups and two were the control groups. All four groups underwent training in credit management between the pre- and post-tests. Results from our research show that while the DSS is effective, improvements need to be made in the methodology to assess such improvements. Moreover, such assessment frameworks, while being adequate from a DSS-centric viewpoint, do not respond to the assessment of a DSS in an organizational setting. In our concluding section, we have discussed how this evaluative framework can be strengthened to initiate an activity that will allow the long-term, and possibly the only meaningful, evaluation framework for such a system.     

An intelligent decision support system for management of petroleum-contaminated sites

Groundwater and soil contamination resulted from LNAPLs (light nonaqueous phase liquids) spills and leakage in petroleum industry is currently one of the major environmental concerns in North America. Numerous site remediation technologies have been developed and implemented in the last two decades. They are classified as ex-situ and in-situ remediation techniques. One of the problems associated with ex-situ remediation is the cost of operation. In recent years, in-situ techniques have acquired popularity. However, the selection of the optimal techniques is difficult and insufficient expertise in the process may result in large inflation of expenses. This study presents an expert system (ES) for the management of petroleum contaminated sites in which a variety of artificial intelligence (AI) techniques were used to construct a support tool for site remediation decision-making. This paper presents the knowledge engineering processes of knowledge acquisition, conceptual design, and system implementation. The results from some case studies indicate that the expert system can generate cost-effective remediation alternatives to assist decision-makers.     

A decision support system for adaptive real-time management of seasonal wetlands in California

This paper describes the development of a comprehensive flow and salinity monitoring system and application of a decision support system (DSS) to improve management of seasonal wetlands in the San Joaquin Valley of California. The Environmental Protection Agency regulates salinity discharges from non-point sources to the San Joaquin River using a procedure known as the total maximum daily load (TMDL) to allocate the assimilative capacity of the river for salt among watershed sources. Management of wetland sources of salt load will require the development of monitoring systems, more integrative management strategies and coordination with other entities. To obtain local cooperation, the Grassland Water District (GWD), whose primary function is to supply surface water to private duck clubs and manage wetlands, needs to communicate to local landowners the likely impacts of salinity regulation on the long-term health and function of wildfowl habitat. The project described in this paper will also provide this information. The models that form the backbone of the DSS, develop salinity balances at both a regional and local scale. The regional scale concentrates on deliveries to and exports from the GWD while the local scale focuses on an individual wetland unit where more intensive monitoring is being conducted. The design of the DSS is constrained to meet the needs of busy wetland managers and is being designed from the bottom up utilizing tools and procedures familiar to these individuals.     

A decision support system for college selection

In this changing society the importance of higher education is increasing more than ever before. The selection of an appropriate university or college is of vital importance to the student for acquisition of proper educational experience. There are thousands of universities and colleges in the United States and Canada. The information available to students about each college is plentiful but is rather tedious to be obtained. The decision process of college selection is further complicated by many factors such as tuition, location, rank, size of the universities, and so forth. These factors play an important role in the final selection of a college. Therefore, a computer-based decision support system is developed in this paper to help users make better decisions in the selection of a college.

This college selection decision support system is designed to be menu-driven and highly user-friendly with a “Help” utility. Many “what-if” scenarios are also available in this system. It can be run on any IBM XT/AT or compatible machines with a DOS environment. It will allow users to make better decisions in their college selection process.     

A manufacturing decision support system for flamecutting

The design of a Manufacturing Decision Support System (MDSS) for the control of a flamecutting operation is discussed. The MDSS incorporates the overall economics of a continuing inventory. It also takes into account the use of left-over offcuts or partial plates, and the possibility of producing as a flow shop instead of on a job shop cut-to-order basis. Some research results are available that aid the construction of such a DSS; these are mostly in the areas of two-dimensional parts' layout and torch path sequencing. The proposed DSS constructs parts' nests, generates cutting sequences, directs the use of trim margins, and updates and outputs the economics of the whole cutting process. Its major strength is its potential use at the shop floor level, using relatively inexpensive computing power to control cutting torches that are usually driven by far more expensive systems that take much longer to determine layouts and cutting sequences.     

A decision support system for fund raising management based on the Choquet integral methodology

The employment of a decision support system for optimizing fund raising strategies is crucial to the management of non‐profit organizations. Commonly considered methodologies utilize the organization's donor database in order to gather and analyse information. However, many organizations, especially small‐ and medium‐sized ones, do not own or efficiently manage a database, and consequently, the usual methods are inapplicable. In this paper, a decision support system is developed that is able to identify the most promising fund raising strategies on the basis of the organization's profile. The profile factors of a non‐profit organization are analysed and hierarchically organized in a decision tree in order to effectively employ the Choquet integral methodology, which is recommended in these kinds of multi‐criteria decision problems. The results obtained in the real operational context show the effectiveness of the proposed system.     

A decision support system for cellular manufacturing system design

With the growth of competitive pressure in the global markets, there has been an increase in demand in industry for cellular manufacturing systems (CMSs) in order to improve productivity and process flexibility. The design of CMSs for industrial applications is a complex and knowledge intensive process as it involves the consideration of many factors including production data and process characteristics. This paper describes the development and implementation of a decision support system for the feasibility and conceptual design of CMSs. The system is based on the knowledge-based system approach, and is able to make recommendations of system feasibility, cell formation techniques and cell types. A case study is also presented to demonstrate the capability of the decision support system.     

A decision support system for the bimodal dial-a-ride problem

A bimodal dial-a-ride problem (BDARP) considered in this paper is a dial-a-ride problem that involves two transportation modes: paratransit vehicles and fixed route buses. Riders in such a system might be transferred between different transportation modes during the service process. The motivation of this research is that by efficiently coordinating paratransit vehicles with fixed route buses we can improve the accessibility and efficiency of a dial-a-ride system. In this paper, we design a decision support system (DSS) which automatically constructs efficient paratransit vehicle routes and schedules for the BDARP. This DSS has been tested using actual data from the Ann Arbor Transportation Authority (AATA) in Ann Arbor, MI. The results show that this DSS produces an average increase of 10% in the number of requests that can be accommodated and an average decrease of 10% in the number of paratransit vehicles required, as compared to the manual results where no fixed route buses are involved     

Using a decision support system in management education

The development of an innovative management decision support system in an educational context is described. Particular attention is devoted to the use of the system as an integral element of an MBA program and to the formal evaluation of the impact of the systems development process and the system on the users attitudes, values, information usage behavior and decision-making behavior.