Organizational Decision Support Research Articles

How organisational factors and clinical decision support system affect nurses’ knowledge for decisions in triage

ABSTRACT This paper delves into heuristic decision-making by nurses during the triage process, aiming to elucidate how organisational factors influence nurses’ decision-making regarding the assignment of priority codes to patients, and to assess the effectiveness of Clinical Decision Support Systems (CDSS) in this context. Drawing on an experimental dataset of 25 triage cases evaluated by 35 nurses via interviews, the study was conducted in two Spanish Emergency Departments using CDSS. Findings indicate that organisational factors predominantly influence decisions in cases with complete and coherent information. However, in cases where information is incoherent or missing, individual nurse characteristics guide decision-making. Furthermore, it suggests that CDSS should be tailored to nurses’ clinical reasoning to serve as effective support for individual decision-making processes.

AUTOMATIC WEIGHT CRITERIA FOR SAW-BASED DECISION SUPPORT SYSTEM USING GRADIENT DESCENT

Modern organizations always utilize a Decision Support Systems (DSS) to have informed decision-making. The Simple Additive Weighting (SAW) method is a prevalent approach used in DSS for evaluating alternatives based on specific criteria. However, the subjectivity inherent in determining criteria weights in SAW poses a significant challenge. This research introduces a new approach, combining the SAW method with Gradient Descent to automatically determine criteria weights. By doing so, it takes advantage of the similarities between SAW and linear equations. Our research shows that this method produces more accurate and unbiased criteria weights, as confirmed by the Mean Square Error (MSE) analysis. In conclusion, incorporating Gradient Descent into the Decision Support System has the potential to greatly improve its effectiveness by automating the criteria weight determination process in various decision-making scenarios, leading to more accurate and less subjective decision support in organizations.

Aid decision on blockchain project: public vs private

Blockchain technology has become a reality and its experimentation promises a future of innovative and beneficial technology. In almost all fields, this technology has seduced the actors because of its potentialities, namely traceability, transparency, security, immutability, etc. However, setting up such a technology adapted to one's needs is far from being a classic project. Indeed, a technical and organizational decision support is very important during a blockchain project to measure its actions and anticipate the risks. In this paper, we will focus on the choice of the public or private blockchain according to technical, functional criteria, and a decision aid module based on the Fuzzy method is proposed to do so. The results obtained prove the effectiveness of our approach used in this paper.

Investing in our human capital: Sharing the experience of implementing a retention strategy at a large academic hospital from a leadership lens

The impact of the COVID-19 pandemic on healthcare providers is well documented and has resulted in significant pressures from a health human resources perspective with many point-of-care providers taking extended leave or moreover, leaving the healthcare sector altogether. As part of a larger Health Human Resources (HHR) strategy at Sunnybrook Health Sciences Centre (Sunnybrook) in Toronto, Canada, a time-limited interprofessional working group titled Supporting Team Sunnybrook (STS) was created. The working group was created to focus on staff retention to respond to ongoing concerns by leaders with regard to staff leaving the organization at an increased rate as documented by our organization's decision support team. Anecdotally, many staff cited their decision to leave the organization as a consequence of the pandemic. As no staff retention committee had been formally created at our organization, STS was established to engage all staff members while addressing and resolving current feedback, concerns, suggestions and issues. The objective of our working group was to review published literature, establish themes from this review, and align these themes to priority themes brought forward by staff through a number of data capture activities. Data capture activities included reviewing existing survey data, new survey data and meetings with staff members. Analysis of the data resulted in the identification of five key consensus areas (priority themes): Staff recognition, wellbeing, grow at Sunnybrook, leaderful leaders, and communication. Our team created five corresponding working groups with the aim to create short- and long-term goals, as well as time sensitive and sustainable operational activities that would contribute to improved staff retention at our organization. Outcomes from our work provided two key learnings to leaders on their ongoing work to retain staff which were the importance of: (1) engagement across all roles, professions including non-clinical team members and support staff and (2) broad communication on the outcomes of our working group to demonstrate that that their feedback was taken seriously and acted upon.

Evaluating the automated organizational decision support effectiveness

In the automating the management process practice, there is currently a challenges number identified by the toolkit ability to evaluate the management automation tools implementation expected effectiveness, including industrial process management. In practice, an automation assessment tools wide range are used, but none of the tools used provides an automated management system quality comprehensive evaluation as a whole and its components separately. This situation makes it difficult to develop and implement automated production management tools. The article suggests applying a methodology that uses the automatic control theory mathematical apparatus to evaluate control automation effectiveness. An example of how to evaluate a typically automated control system effectiveness is given. This example analysis shows the proposed approach’ practical applicability. The proposed mathematical apparatus practical application will ensure an express assessment when comparing the various options for providing control automation, which is especially important at the pre-project research and project stages.

Complexity drivers in engineering design

PurposeIn the era of industry 4.0, managing the design is a challenging mission. Within a dynamic environment, several disciplines have adopted the complex adaptive system (CAS) perspective. Therefore, this paper aims to explore how we may deepen our understanding of the design process as a CAS. In this respect, the key complexity drivers of the design process are discussed and an organizational decomposition for the simulation of the design process as CAS is conducted.Design/methodology/approachThe proposed methodology comprises three steps. First, the complexity drivers of the design process are presented and are matched with those of CAS. Second, an analysis of over 111 selected papers is presented to choose the appropriate model for the design process from the CAS theory. Third, the paper provides methodological guidelines to develop an organizational decision support system that supports the complexity of the design process.FindingsAn analysis of the key drivers of design process complexity shows the need to adopt the CAS theory. In addition to that, a comparative analysis between all the organizational methodologies developed in the literature leads the authors to conclude that agent-oriented Software Process for engineering complex System is the appropriate methodology for simulating the design process. In this respect, a system requirements phase of the decision support system is conducted.Originality/valueThe originality of this paper lies in the fact of analysing the complexity of the design process as a CAS. In doing so, all the richness of the CAS theory can be used to meet the challenges of those already existing in the theory of the design.

Support decision-making in information control systems using the temporal knowledge base

The subject matter of the article are the processes of using temporal knowledge to support decision-making on the control of composite objects within the framework of the enterprise functioning paradigm. The goal is to develop an integrated approach to building, as well as the use of temporal knowledge bases for analyzing the current state of the enterprise as an integral object at different levels of the organizational hierarchy and decision support for control. Tasks: to develop a model of the temporal knowledge base for the presentation of context-oriented temporal dependencies on the behavior of the control object; improve the method of detecting anomalous states of the control object based on the analysis of temporal data and knowledge; to present technologies for the automated construction and use of a temporal knowledge base to support decision-making on enterprise management. The methods used are: methods of construction knowledge bases and methods of supporting management in conditions of uncertainty. The following results were obtained. The following results were obtained. A model of a temporal knowledge base has been developed for use in information management systems. The method of detecting anomalous states of the control object in information control systems based on the use of temporal dependencies has been improved. Technologies for building and using a temporal knowledge base to support management decisions under uncertainty are proposed. Conclusions. The scientific novelty of the results obtained is as follows: A model of a temporal knowledge base has been developed, containing patterns and the realization of logical facts reflecting the state of the control object, as well as the rules establishing the links between these states over time. The model makes it possible to increase the efficiency of enterprise management in conditions of uncertainty based on an analysis of its current state and the derivation of permissible sequences of actions for the transition to a target state. The method for detecting anomalous states of a control object in information control systems has been improved by taking into account the connection between actions at the control object and the context for performing these actions. The method allows to take into account the current properties of the components of the complex control object to classify the current state in conditions of uncertainty. The proposed technology for the construction and use of a temporal knowledge base in information management systems that provide for the iterative replenishment of knowledge in the operation of an enterprise and their use in the context of incomplete information about the control object.

A Framework for “Just-in-Time Learning” Decision Support in Organizations

This article describes an integrated “Just-in-Time Learning” framework for providing decision support in organizations. The framework emerges from years of work with the national laboratories and facilities that are under the direction of the United States Department of Energy. The article begins by describing expert systems technology and how it has been used to provide decision support in organizations. This is followed by a discussion of the strengths and weaknesses of expert systems technology for this purpose. Next, a “Just-in-Time Learning” framework is introduced where the theoretical foundation for the framework is described. Afterwards, the other aspects of the framework including the types of knowledge, learners it serves, and how the framework can be utilized for decision support are detailed. Finally, a discussion section summarizes how a Just-in-Time Learning Framework can achieve some of the strengths -- while overcoming some of the weaknesses -- of expert system technology for providing decision support in organizations.

Agent Based Simulation on Task Workflow Oriented Self-Organization of Cellular Organizational Network

Modeling self-organization is a critical problem in computational analysis of organizational dynamics and decision support system. Task workflow oriented self-organization shows different characteristics from completely self-organization mechanism. Towards this end, a task workflow oriented self-organization model is constructed to analyze the internal mechanism and performance under different task requirement environments. The cellular organizational network is used to model the relationship structure of cellular work groups. The Agent Based Simulation (ABS) is used to simulate the whole activity self-organization process and individual behaviors driven by task workflow. The model is examined through for simulation experiments. Experimental results shows the difference of the organizational performance according to different task requirements, which exhibits potential application in task workflow oriented organization design and optimization.

Unsupervised Learning Framework for Customer Requisition and Behavioral Pattern Classification

Maintaining healthy organization-customers relationship has positive influence on customers’ behavioral tendencies as regards preference to products and services, buying behavior, loyalty, satisfaction, and so on. To achieve this, an in-depth analysis of customers’ characteristics and purchasing behavioral trend is required. This paper proposes a hybrid unsupervised learning framework consisting of k-means algorithm and self-organizing maps (SOMs) for customer segmentation and behavior analysis. K-means algorithm was used to partition the entire input space of customers’ transaction dataset into 3 and 4 disjoint segments based on customers’ frequency (F) and monetary value (MV). SOM provided visualization of the underlying clusters and discovered customers’ relationships in the dataset. Interaction of F and MV clusters resulted in 12 sub-clusters. An in-depth analysis of each sub-cluster was also performed and appropriate customer relationship management (CRM) strategies established for each sub-cluster. Discovered knowledge will guide effective allocation of resources to each customer cluster and other organizational decision support functions much required by CRM systems.

Improving organizational decision support: Detection of outliers and sales prediction for a pharmaceutical distribution company

Stock unavailability in the supply of medicines to pharmacies can be caused by several factors including manufacturing problems, lack of raw materials, end of product selling, disease and epidemics outbreaks. Furthermore, the sale of medicines by some pharmacies to foreign markets has increased in recent years, and is considered one of the main causes of medicine supply failures in Portugal. This paper depicts the case study of a pharmaceutical distribution company in Portugal and aims to address two main research issues. The first one consisted in detecting customers (pharmacies) and products (medicines) which may be considered outliers and perform stock proration when these outliers are detected, in order to avoid abnormal sales and out-of-stocks in pharmacies. The second one targeted the sales prediction for the pharmaceutical distribution company, in order to better control and manage the levels of stock of medicines, so as to avoid excessive inventory costs while guaranteeing customer demand satisfaction, and thus decreasing the possibility of loss of customers due to stock outages. In outliers detection (customers and products) we used the Box-plot method as well as the SPSS statistical software. For sales prediction, the time series data mining method smoothed Pegels was used, while the implementation was done in SQL and the analyzed data was stored in an Oracle database.

An integrated environment for organisational decision support

The challenges and requirements associated with the provision of organisational decision support are presented with the aim of supporting the through-life development of network enabled capability (NEC) systems. These requirements were compiled through a review of pertinent literature as well as through a series of workshops held with the industrial sponsor of this research – BAE Systems. An architecture is described here for organisational decision support that was developed from the requirements and was subsequently used to create a prototype integrated decision support environment (IDSE). The IDSE was developed incrementally, adding functionality relating to the provision of organisational decision support (DS) with the performance evaluation after each development stage. A scenario and associated case-study were developed within the NECTISE project and presented here, which allowed the demonstration and evaluation of the first prototype IDSE within four separate live simulations. The evaluation feedback from BAE Systems and the Ministry of Defence representatives suggests that the architecture and prototype implementation of the IDES for the provision of organisational decision support are both relevant and mature for future industrial application.

Pest risk analysis of Metcalfa pruinosa in Austria

The North American planthopper Metcalfa pruinosa (Say, 1830) (Hemiptera: Flatidae) was accidentally introduced into Europe, and subsequently caused economic damage to orchards and vineyards in some South-European countries. In 2003, a mass occurrence of M. pruinosa was discovered in Vienna, followed by new infestations of several sites. A Pest Risk Analysis was conducted, according to the European and Mediterranean Plant Protection Organization decision support scheme for quarantine pests, to evaluate the risk to Austrian agriculture by M. pruinosa. The highest risk of further introduction into Austria arises from trade of deciduous plants infested with M. pruinosa eggs from Italy and France where this pest is common and which are important trading partners of Austria. Entry by vehicle traffic is considered moderately likely. Active spread by flight of adult M. pruinosa is considered significant only for local dispersal. The CLIMEX® program was applied to predict M. pruinosa’s potential geographical distribution and to identify areas at risk. In Austria, southern Burgenland and south-east Styria as well as parts of Vienna, Lower and Upper Austria provide the most suitable climate for M. pruinosa’s development. Organic production areas in theses regions are especially at risk of being damaged. To prevent economic impact and for long-term control of M. pruinosa, biological control with its natural enemy Neodryinus typhlocybae (Ashmead 1893) (Hymenoptera: Dryinidae) is recommended.

Towards the Realization of an Integrated Decision Support Environment for Organizational Decision Making

Traditional decision support systems are based on the paradigm of a single decision maker working at a standalone computer or terminal who has a specific decision to make with a specific goal in mind. Organizational decision support systems aim to support decision makers at all levels of an organization (from executive, middle management managers to operators), who have a variety of decisions to make, with different priorities, often in a distributed and dynamic environment. Such systems need to be designed and developed with extra functionality to meet the challenges such as collaborative working. This article proposes an Integrated Decision Support Environment (IDSE) for organizational decision making. The IDSE distinguishes itself from traditional decision support systems in that it can flexibly configure and re-configure its functions to support various decision applications. IDSE is an open software platform which allows its users to define their own decision processes and choose their own exiting decision tools to be integrated into the platform. The IDSE is designed and developed based on distributed client/server networking, with a multi-tier integration framework for consistent information exchange and sharing, seamless process co-ordination and synchronisation, and quick access to packaged and legacy systems. The prototype of the IDSE demonstrates good performance in agile response to fast changing decision situations.

A Knowledge Integration Approach for Organizational Decision Support

This study proposes a new methodology that facilitates organizational decision support through knowledge integration across organizational units. For this purpose, this study develops a decision support loop and explains how to organize individual knowledge related to a specific business problem and formulate and test the organized knowledge using cognitive modeling techniques for decision support. This study discusses the proposed approach in the context of an application case involving a beverage company. The application case shows the validity and usefulness of the proposed approach.

Method for designing organization decision support system framework

The concept of organization decision support system (ODSS) is defined according to practical applications and novel understanding. And a framework for ODSS is designed. The framework has three components: infrastructure, decision-making process and decision execution process. Infrastructure is responsible to transfer data and information. Decision-making process is the ODSS's soul to support decision-making. Decision execution process is to evaluate and execute decision results derived from decision-making process. The framework presents a kind of logic architecture. An example is given to verify and analyze the framework. The analysis shows that the framework has practical values, and has also reference values for understanding ODSS and for theoretical studies.

An adaptive systems toolkit for managing the Hawkesbury water recycling scheme

To achieve effective, sustainable and acceptable water recycling, the need for integrated and multidisciplinary management is now well recognised. The complexity of interacting technical, socio-cultural and environmental factors requires approaches which are responsible, robust and adaptable. This paper describes the toolkit which is being developed for managing the Hawkesbury Water Recycling Scheme. A management plan based upon the logic of environmental management systems provides the basic structure of the toolkit, with supporting systems including those relating to organisational support, decision support, monitoring and research, communication, and an external interface. Lessons learned to date suggest that this form of management approach can assist in operationalising principles of sustainability and adaptive management in relation to water recycling.

An organizational decision support system for effective R&D project selection

Research and development (R&D) project selection is an important task for organizations with R&D project management. It is a complicated multi-stage decision-making process, which involves groups of decision makers. Current research on R&D project selection mainly focuses on mathematical decision models and their applications, but ignores the organizational aspect of the decision-making process. This paper proposes an organizational decision support system (ODSS) for R&D project selection. Object-oriented method is used to design the architecture of the ODSS. An organizational decision support system has also been developed and used to facilitate the selection of project proposals in the National Natural Science Foundation of China (NSFC). The proposed system supports the R&D project selection process at the organizational level. It provides useful information for decision-making tasks in the R&D project selection process.

A Web-based AHP approach to standardize the process of managing service-contracts

Despite the gainful use of the Web as a communication medium, recent research suggests that very little work has been done to leverage this new technology for enabling changes in the planning process. This paper, motivated by a situation at a Fortune 100 company, describes a prototype Web-based decision support system (DSS) for the management of service contracts, which are being increasingly purchased by a variety of businesses. The planning for spare parts needed to support service contracts is hampered by unpredictable and extremely low demands, thereby necessitating a subjective manual approach that is inherently non-standardized and non-coordinated among the various constituents of the organization. In this paper, we describe how a Web-based system can enable the standardization of the process of managing and supporting service contracts. In addition, our DSS supports geographically and functionally dispersed decision-making roles, thereby displaying characteristics of an organizational decision support system (ODSS).

The new dynamics of strategy: Sense-making in a complex and complicated world

In this paper, we challenge the universality of three basic assumptions prevalent in organizational decision support and strategy: assumptions of order, of rational choice, and of intent. We describe the Cynefin framework, a sense-making device we have developed to help people make sense of the complexities made visible by the relaxation of these assumptions. The Cynefin framework is derived from several years of action research into the use of narrative and complexity theory in organizational knowledge exchange, decision-making, strategy, and policy-making. The framework is explained, its conceptual underpinnings are outlined, and its use in group sense-making and discourse is described. Finally, the consequences of relaxing the three basic assumptions, using the Cynefin framework as a mechanism, are considered.