Use of an environmental diagnostic study on a coastal lagoon as a decision support tool for environmental management policies in a coastal zone

2020 ◽  
Vol 31 (1) ◽  
pp. 167-184
Author(s):  
Pollyana C.V. Morais ◽  
Marcielly F.B. Lima ◽  
Davi A. Martins ◽  
Lysandra G. Fontenele ◽  
Joyce L.R. Lima ◽  
...  

Purpose An efficient and adequate environmental monitoring plan is essential to any integrated coastal zone management (ICZM) program. The purpose of this paper is to apply an environmental diagnostic study to a coastal lagoon using anthropogenic markers as a decision support tool to aid the development of coastal environmental management policies. Design/methodology/approach Specifically, environmental status and anthropogenic sources were determined as part of a coastal environmental management plan; a study of human occupation and use was conducted to determine the predominant human activities around the lagoon; an environmental diagnostic study was conducted to determine the occurrence, levels and distribution of markers; and the results of the environmental diagnostic study were compared to indicators stipulated in Brazilian legislation. Findings Land use study revealed both urban and rural activities around the lagoon, as evidenced by the existence of residences, restaurants as well as poultry and livestock activities. The environmental diagnostic study revealed the input of human sewage (treated and raw) and runoff from animal husbandry activities. Practical implications The information produced using anthropogenic markers showed the influence of less studied rural activities, such as livestock and poultry farming, thereby providing a more reliable environmental status compared to the use of classic indicators employed in laws issued by international and Brazilian agencies. Originality/value The present results show that classic indicators used by environmental agencies are insufficient for an accurate diagnosis of coastal zones with multiple anthropogenic activities. Thus, the modernization of the environmental monitoring plan of the ICZM program is urgently needed for a more accurate assessment of coastal environments.

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Serhat Simsek ◽  
Abdullah Albizri ◽  
Marina Johnson ◽  
Tyler Custis ◽  
Stephan Weikert

PurposePredictive analytics and artificial intelligence are perceived as significant drivers to improve organizational performance and managerial decision-making. Hiring employees and contract renewals are instances of managerial decision-making problems that can incur high financial costs and long-term impacts on organizational performance. The primary goal of this study is to identify the Major League Baseball (MLB) free agents who are likely to receive a contract.Design/methodology/approachThis study used the design science research paradigm and the cognitive analytics management (CAM) theory to develop the research framework. A dataset on MLB's free agents between 2013 and 2017 was collected. A decision support tool was built using artificial neural networks.FindingsThere are clear links between a player's statistical performance and the decision of the player to sign a new offered contract. “Age,” “Wins above Replacement” and “the team on which a player last played” are the most significant factors in determining if a player signs a new contract.Originality/valueThis paper applied analytical modeling to personnel decision-making using the design science paradigm and guided by CAM as the kernel theory. The study employed machine learning techniques, producing a model that predicts the probability of free agents signing a new contract. Also, a web-based tool was developed to help decision-makers in baseball front offices so they can determine which available free agents to offer contracts.


2020 ◽  
Vol 25 (2) ◽  
pp. 183-199 ◽  
Author(s):  
Zhe Zhang ◽  
Zhi Ye Koh ◽  
Florence Ling

Purpose This study aims to develop benchmarks of the financial performance of contractors and a decision support tool for evaluation, selection and appointment of contractors. The financial benchmarks allow contractors to know where they are relative to the best-performing contractors, and they can then take steps to improve their own performance. The decision support tool helps clients to decide which contractor should be awarded the project. Design/methodology/approach Financial data between 2013 and 2015 of 44 Singapore-based contractors were acquired from a Singaporean public agency. Benchmarks for Z-score and financial ratios were developed. A decision tree for evaluating contractors was constructed. Findings This study found that between 57% and 64% of contractors stayed in the financially healthy zone from 2013 to 2015. Ratios related to financial liabilities are relatively bad compared with international standards. Research limitations/implications The limitation is that the data is obtained from a cross-sectional survey of contractors’ financial performance in Singapore over a three-year period. Regarding the finding that ratios relating to financial liabilities are weak, the implication is that contractors need to reduce their financial liabilities to achieve a good solvency profile. Contractors may use the benchmarks to check their financial performances relative to that of their competitors. To reduce financial risks, project clients may use these benchmarks to examine contractors’ financial performance. Originality/value This study provides benchmarks for contractors and clients to examine the financial performance of contractors in Singapore. A decision tree is provided to aid clients in making decisions on which contractors to appoint.


2016 ◽  
Vol 27 (7) ◽  
pp. 898-914 ◽  
Author(s):  
Nicholas A. Meisel ◽  
Christopher B. Williams ◽  
Kimberly P. Ellis ◽  
Don Taylor

Purpose Additive manufacturing (AM) can reduce the process supply chain and encourage manufacturing innovation in remote or austere environments by producing an array of replacement/spare parts from a single raw material source. The wide variety of AM technologies, materials, and potential use cases necessitates decision support that addresses the diverse considerations of deployable manufacturing. The paper aims to discuss these issues. Design/methodology/approach Semi-structured interviews with potential users are conducted in order to establish a general deployable AM framework. This framework then forms the basis for a decision support tool to help users determine appropriate machines and materials for their desired deployable context. Findings User constraints are separated into process, machine, part, material, environmental, and logistical categories to form a deployable AM framework. These inform a “tiered funnel” selection tool, where each stage requires increased user knowledge of AM and the deployable context. The tool can help users narrow a database of candidate machines and materials to those appropriate for their deployable context. Research limitations/implications Future work will focus on expanding the environments covered by the decision support tool and expanding the user needs pool to incorporate private sector users and users less familiar with AM processes. Practical implications The framework in this paper can influence the growth of existing deployable manufacturing endeavors (e.g. Rapid Equipping Force Expeditionary Lab – Mobile, Army’s Mobile Parts Hospital, etc.) and considerations for future deployable AM systems. Originality/value This work represents novel research to develop both a framework for deployable AM and a user-driven decision support tool to select a process and material for the deployable context.


2020 ◽  
Vol 5 (1) ◽  
pp. 121-136
Author(s):  
Christos Papaleonidas ◽  
Dimitrios V. Lyridis ◽  
Alexios Papakostas ◽  
Dimitris Antonis Konstantinidis

Purpose The purpose of this paper is to improve the tactical planning of the stakeholders of the midstream liquefied natural gas (LNG) supply chain, using an optimisation approach. The results can contribute to enhance the proactivity on significant investment decisions. Design/methodology/approach A decision support tool (DST) is proposed to minimise the operational cost of a fleet of vessels. Mixed integer linear programming (MILP) used to perform contract assignment combined with a genetic algorithm solution are the foundations of the DST. The aforementioned methods present a formulation of the maritime transportation problem from the scope of tramp shipping companies. Findings The validation of the DST through a realistic case study illustrates its potential in generating quantitative data about the cost of the midstream LNG supply chain and the annual operations schedule for a fleet of LNG vessels. Research limitations/implications The LNG transportation scenarios included assumptions, which were required for resource reasons, such as omission of stochasticity. Notwithstanding the assumptions made, it is to the authors’ belief that the paper meets its objectives as described above. Practical implications Potential practitioners may exploit the results to make informed decisions on the operation of LNG vessels, charter rate quotes and/or redeployment of existing fleet. Originality/value The research has a novel approach as it combines the creation of practical management tool, with a comprehensive mathematical modelling, for the midstream LNG supply chain. Quantifying future fleet costs is an alternative approach, which may improve the planning procedure of a tramp shipping company.


2015 ◽  
Vol 26 (2) ◽  
pp. 296-312 ◽  
Author(s):  
Kristina Liljestrand ◽  
Martin Christopher ◽  
Dan Andersson

Purpose – The purpose of this paper is to develop a transport portfolio framework (TPF) and explore its use as a decision support tool for shippers wanting to improve their transport system in terms of reducing their carbon footprint. Design/methodology/approach – The TPF has been designed on the basis relevant theoretical frameworks in logistics and thereafter tested and further developed by the use of empirical data from a case study. Quantitative methods are used to find patterns in the shipment statistics for import flows obtained from a food retailer and carriers. Findings – The TPF highlights different avenues for decreasing the carbon footprint, by identifying the product flow characteristics that might affect modal split and load factor, and it is believed that these can help shippers’ intent on analysing the largest potential for improvement. This potential is estimated based on how the key variables, modal split and load factor, can be improved. Practical implications – Shippers can use the TPF as a decision support tool in their efforts to reduce their carbon footprint by: structuring complexity, managing data and finding effective solutions. Social implications – Reducing emissions is an increasingly important priority for shippers and the TPF helps them to direct their efforts towards approaches that have a substantial impact. Originality/value – The TPF provides an opportunity to match different approaches for improving the environmental performance with the potential for reducing carbon footprint in shippers’ transportation networks, by taking into account the complexity of logistics network.


2017 ◽  
Vol 6 (1) ◽  
pp. 2-18 ◽  
Author(s):  
Natee Singhaputtangkul

Purpose There are a number of decision-making problems encountered by a building design team. This issue is apparent in assessment of building envelope materials and designs in the early design stage. The purpose of this paper is to develope a decision support tool based on a quality function deployment (QFD) approach integrated with a knowledge management system (KMS) and fuzzy theory to facilitate a building design team to simultaneously mitigate the decision-making problems when assessing the building envelope materials and designs for the first instance. Design/methodology/approach This study engaged a design team comprising three decision makers (DMs) to test the developed decision support tool through a case study of a representative building project. The study employed deductive qualitative data analysis with use of a framework analysis approach to analyze perspectives of the DMs after completing the case study through a semi-structured interview. Findings A mapping diagram derived qualitatively from the framework analysis suggested that the tool can help mitigate the identified decision-making problems as a whole. Originality/value Practical contributions of using the decision support tool include achievement of a more efficient design and construction management, and higher productivity of a project. In terms of academic contributions, this study expands capabilities of a conventional decision support system, KMS, and QFD tool to handle decision-making problems.


2019 ◽  
Vol 17 (3) ◽  
pp. 249-266
Author(s):  
Michael A. Beauregard ◽  
Steven K. Ayer

Purpose The discretionary expense budget required to maintain public infrastructure has declined in recent years, even as public expectations and accountability for performance have increased. The purpose of this paper is to leverage previously reported research to create a decision support tool (DST) for prioritizing institutional facility maintenance. Design/methodology/approach A structured literature review was developed to identify critical aspects of facility maintenance shown to have a positive relationship with academic performance in K-12 schools within the USA. Analytical hierarchy process (AHP) serves as a framework for a multi-criteria DST based on the findings of the literature review. Finally, a targeted focus group of industry professionals was used to validate the usability of the resulting DST. Findings The framework for the DST developed for this study effectively represents the scale and scope of an institutional facility. Results of the study suggest that when evaluating multi-criteria work orders, the proposed visual AHP methodology can be used to generate usable DSTs to assist with the prioritization of work. Practical implications This study provides a methodology for building a multi-criterion DST leveraging precedent research, using a visual AHP to assist facility management (FM) decision-makers in the prioritization of routine work orders. Originality/value The developed process indicates a practical approach to incorporating disparate research findings into a concise and useable manner to guide FM decision-makers, who have traditionally not been able to explicitly leverage this information to make evidence-based spending decisions.


2016 ◽  
Vol 36 (4) ◽  
pp. 412-428
Author(s):  
Pedro Neves ◽  
Luis Ribeiro ◽  
João Dias-Ferreira ◽  
Mauro Onori ◽  
José Barata Oliveira

Purpose This paper aims to provide a method and decision support tool to enhance swift reconfiguration of Plug&Produce (P&P) systems in the presence of continuously changing production orders. Design/methodology/approach The paper reviews different production scenarios and system design and configuration methods and more particularly specifies the need of decision support tools for P&P systems that integrate configuration and planning activities. This problem is then addressed by proposing a method that helps reduce the solution space of the reconfiguration problem and allows the timely selection of the most promising reconfiguration alternative. Findings The proposed method was found to be helpful in reducing the reconfiguration alternatives that need to be considered and in selecting the most promising one for different orders. The advantages and limitations of this method are identified, and an illustrative test case of the approach is presented, corroborating the method applicability in the absence of large queues in the system. Originality/value This paper addresses a less explored domain within the P&P systems research field, which is the system reconfiguration. It proposed a method to support system validation and reconfiguration jointly with an illustrative test case. This represents an original contribution to the P&P research field, and it can have impact in improving agility and decreasing the complexity of reconfiguration activities to cope with constantly changing production orders.


2016 ◽  
Vol 6 (3) ◽  
pp. 332-344 ◽  
Author(s):  
Weena Lokuge ◽  
Nirdosha Gamage ◽  
Sujeeva Setunge

Purpose – Deterioration of timber bridges can often be related to a number of deficiencies in the bridge elements, connectors and/or as a result of been in aggressive environments which they are exposed to. The maintenance cost of timber bridges is affected significantly by a number of deterioration mechanisms which require a systematic approach for diagnosis and treatment. Evaluating the risk of failure of these bridges is of importance in bridge performance assessment and decision making to optimize rehabilitation options. The paper aims to discuss these issues. Design/methodology/approach – This paper identifies common causes for timber bridge deterioration and demonstrates an integrated approach based on fault tree analysis to obtain qualitative or quantitative estimation of the risk of failure of timber bridge sub-systems. Level 2 inspection report for a timber bridge in Queensland, Australia has been utilized as a case study in this research to identify the failure modes of the bridge. Findings – A diagnostic tool for timber bridge deterioration will benefit asset inspectors, managers, and engineers to identify the type, size and the distress mechanisms in order to recognize the proper corrective measures either to prevent or to reduce further deterioration. Timber bridge maintenance is a major issue in Queensland, Australia. If a decision support tool can be developed, it will benefit road authorities and local councils. Originality/value – Timber bridge maintenance is a major issue in Queensland, Australia. If a decision support tool can be developed as initiated in this research paper it will benefit road authorities and local councils.


2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ming-Huan Shou ◽  
Zheng-Xin Wang ◽  
Dan-Dan Li ◽  
Yi-Tong Zhou

PurposeSince the issuance in 2009, the digital currency has enjoyed an increasing popularity and has become one of the most important options for global investors. The purpose of this paper is to propose a hybrid model ( KDJ–Markov chain) which integrates the advantages of the stochastic index (KDJ) and grey Markov chain methods and provide a useful decision support tool for investors participating in the digital currency market.Design/methodology/approachTaking Litecoin's closing price prediction as an example, the closing prices from May 2 to June 20, 2017, are used as the training set, while those from June 21 to August 9, 2017, are used as the test set. In addition, an adaptive KDJ–Markov chain is proposed to enhance the adaptability for dynamic transaction information. And the paper verifies the effectiveness of the KDJ–Markov chain method and adaptive KDJ–Markov chain method.FindingsThe results show that the proposed methods can provide a reliable foundation for market analysis and investment decisions. Under the circumstances the accuracy of the training set and the accuracy of the test set are 76% and 78%, respectively.Practical implicationsThis study not only solves the problems that KDJ method cannot accurately predict the next day's state and the grey Markov chain method cannot divide the states very well, but it also provides two useful decision support tools for investors to make more scientific and reasonable decisions for digital currency where there are no existing methods to analyze the fluctuation.Originality/valueA new approach to analyze the fluctuation of digital currency, in which there are no existing methods, is proposed based on the stochastic index (KDJ) and grey Markov chain methods. And both of these two models have high accuracy.


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